SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
Authors: Junyan Cheng
Peter Chin
Venue: ICLR 2024 spotlight (2024)
Keywords: Large Langauge Models
Agent
Prompt Tunning
Time series forcasting
Primary area: datasets and benchmarks
TL;DR: We introduce SocioDojo, a lifelong learning environment that lets agents continuously make investment decisions based on news and a novel agent architecture with Hypothesis & Proof prompting for generating in-depth analyses to assist decision-making.

Abstract: We introduce SocioDojo, an open-ended lifelong learning environment for developing ready-to-deploy autonomous agents capable of performing human-like analysis and decision-making on societal topics such as economics, finance, politics, and culture. It consists of (1) information sources from news, social media, reports, etc., (2) a knowledge base built from books, journals, and encyclopedias, plus a toolbox of Internet and knowledge graph search interfaces, (3) 30K high-quality time series in finance, economy, society, and polls, which support a novel task called "hyperportfolio", that can reliably and scalably evaluate societal analysis and decision-making power of agents, inspired by portfolio optimization with time series as assets to "invest". We also propose a novel Analyst-Assistant-Actuator architecture for the hyperportfolio task, and a Hypothesis & Proof prompting for producing in-depth analyses on input news, articles, etc. to assist decision-making. We perform experiments and ablation studies to explore the factors that impact performance. The results show that our proposed method achieves improvements of 32.4% and 30.4% compared to the state-of-the-art method in the two experimental settings.

Published as a conference paper at ICLR 2024
SOCIODOJO: BUILDING LIFELONG ANALYTICAL AGENTS WITH REAL-WORLD TEXT AND TIME SERIES
Junyan Cheng
Peter Chin
Thayer School of Engineering
Thayer School of Engineering
Dartmouth College Hanover, NH 03755, USA je. wt..hhdartmouth.ccmm edu
Dartmouth College
Hanover, NH 03755, USA pc@dartmouth. edu
ABSTRACT
We introduce SocioDojo, an open-ended lifelong learning environment for de- veloping ready-to-deploy autonomous agents capable of performing human-like analysis and decision-making on societal topics such as economics, finance, pol- itics, and culture. It consists of (1) information sources from news, social media, reports, etc., (2) a knowledge base built from books, journals, and encyclope- dias, plus toolbox of Internet and knowledge graph search interfaces, (3) 30K high-quality time series in finance, economy, society, and polls, which support novel task called "hyperportfolio", that can reliably and scalably evaluate so- cietal analysis and decision-making power of agents, inspired by portfolio opti- mization with time series as assets to "invest". We also propose a novel Analyst- Assistant-Actuset architecture for the hyperportfolio task, and a Hypothesis & Proof prompting for producing in-depth analyses on input news, articles, etc. to assist decision-making. We perform experiments and ablation studies to ex- plore the factors that impact performance. The results show that our proposed method achieves improvements of 32.4% and 30.4% compared to the state-of- the-art method in the two experimental settings.
INTRODUCTION
Autonomous agents based on large language models (LLMs) have made considerable progress in task-oriented settings, such as software development (Qian al. (023), web navigation (Zhou et al.) 2023), and robot planning (Chen & Chang 2023). These tasks usually require only task-specific skills rather than open-ended knowledge about human society. For instance, make economic and financial decisions, one must keep track of the latest events and trends as well as have a broad un- ubiquitous in the human world from personal finance and business planning to social science studies and public affairs. Therefore, it is essential to develop agents that can comprehend, analyze, and make decisions in open-ended human society to achieve human- AI.
derstanding of how society works. Such scenarios
Human society is built on information. We create SocioDojo, lifelong learning environment that emulates the historical development of society with real-world texts and time series. SocioDojo has three components: information sources, time series, and a knowledge base & toolbox. It uses continuous stream of messages from multiple sources, such as news, articles, etc., to simulate the dynamics of society. Agents are constantly updated with the latest messages to keep track of changes in society. Time series from finance, economics, search trends, and poll tracking are used to depict the inner states of society. The knowledge base, made up of books, journals, and encyclopedias, along with toolbox that provides Internet access, gives the necessary background knowledge. The use of real data also allows SocioDojo to automatically extend and the agents developed on it to be transferred into real applications without any gaps.
A scalable way to assess the societal analysis and decision decision-making capabilities of agents is needed, approach is expensive in generating up-to-date and high-quality
as the popular auestion-answerte questions that keep up with societal changes. We introduce the hyperportfolio task, which evaluates the agent through their "investment" decisions over time, which requires a thorough understanding

---

Published as a conference paper at ICLR 2024
Present "Price"
History
Future (Invicible to agent)
with initial "cash"
compute total "asset"
Current time
moving forward
Roal-warld Time series
Oct, 1, 2021
Economy
Aug. 2023
Keep Making
Information
"Investment" decisions
Knowledge Bose
Receiving Current News
Query with current time
Books
Journa'
Encydlpedia
Newssoope
Papers
Reports
Tools
Wibsite
Blogge
Social Media
Lifelong Agent
Web Sunrch
Willipedi
Knowledge
Graph
Figure 1: Illustration of the hyperportfolio task in SocioDojo. The agent begin with initial cash of one million on October I, 2021. Messages from information sources will be continually popping out to the agent, with the current time set as the published time of the newly popped message. The agent can "invest" the "assets" that correspond to the time series which "price" is the current value in the time series, based the message, knowledge base, and tools. The goal is maximize the total asset by the end time, August 1, 2023.
of society and sophisticated analysis even for human experts. We extend investable assets from financial assets to all kinds of time series in SocioDojo. An illustration of the hyperportfolio task is shown in Figure] I
SocioDojo presents three unique open challenges. (1) Spatiotemporal and entity information. The time, place, and entity involved in an event are essential. Agents may need to link clues from multiple times, locations, and entities to reach a conclusion.
(2) Expert knowledge in the open domain. To make analysis decision about society, agents may require expert knowledge in multiple undetermined domains. (3) Complex and hybrid reasoning. Agents need both logical and numerical reasoning and the of tools, including even machine learning models, to perform in- depth analysis. Addressing these challenges also facilitates the exploration of general intelligence.
We also present novel Aessttsttssstttstttttett for the hyperportfolio task, which improves the state-of-the-art method by 32.4% and 30.4% two settings in experiment. We also conduct ablation studies on information sources and foundation models. Our key contributions are as follows.
architecture and Syyothesis-Proos prompting
We create SocioDojo with Hyperportfolio task, the first large-scale and casily scalable en- vironment built with real-world texts, time series, and search interfaces, enabling develop- ment and reliable assessment of the analytical and decision- makiin agents for real society;
We propose Aasssystttssssst--Attttts producing in-depth analyses supporting high-quality decision-making:
architecture and Hypothesis-Proo Proof prompting for
We perform experiments and ablation studies explore factors that impact performance.
Our code and data are available at
2
RELATED WORK
Agent Learning Environment. Most learning environments focus on the execution of specific tasks. For example, Zhou et al. (023); Yao al. (2023a) created simulated environments develop
2

---

Published as a conference paper at ICLR 2024
web agents that perform web browsing tasks. Toyama et al. (2021) proposed the use of an Android simulator to learn phone operations. Puig et al. (2018); Shridhar et al. (2020) 2021) aimed to train
embodied agents to reason in home environment. Non-task-specific open- ended agents have been researched through video games. Fan et al. (202)): JJhnsson et al.] (0166; /hah et al. (202)) pro- posed the use of the open- open-world MineCraft game to study a skill set of exploration, planning, etc. for agents, while Vinyals et al. (2017) attempted to use the real-time strategic game StarCraft II to promote strategic thinking and multi-agent planning. Simulated human societies have been stud- ied in empire-building games, with Branavan et al. (201T) developed agents in the Civilizaaion II environment. However, the analytical skills learned in games largely deviate from real society.
Reasoning in Large Language Model Agent. Reasoning is a fundamental skill for agents per- forming analysis. Wei et al. (2022) encourages the model to reason step by step. Yao et al. (2023b) further introduces tree search into the reasoning process. WWng et al. 2023b) suggests using multi- ple reasoning paths instead of a single greedy approach to ensure better se self-consistency. However, these approaches assume that the analysis process can be broken down into atomic steps, which may not be applicable to open-ended problems. Yao et al.] (2023c) proposes a framework that allows the agent adjust its actions based on the effects of those actions. [Shinn et al. (2023) introduces reinforcement learning to refine the adjustment. However, this method requires immediate rewards, which are not available our setting. Press et al. (2023) encourages the agent to ask questions that are beneficial to problem-solving. Wann et al. (2023a) allows the agent to devise a plan before trying to solve a problem. However, the questions and steps in such plans are usually simple fact checks or atomic operations such as calculations, which are not essential for open-ended analysis.
Time-series Forecasting in Social Science. Predictability in finance has been studied under the efficient market hypothesis (Famal 1970) which implies that it takes time for information to be reflected in the price, leaving room for predicting the price at some future time. Financial prediction has been studied in various fields, such as machine learning (Krollner et al., 2010 Kelly & Xiu
2023 Kim 2003), deep learning (Sezer et al. 2020). LLMs (Lopez- Lira & Tang (023). and natural
language processing, which is used to capture sentiment levels and other features (Xing al.) 2018) Lukauskas et al. 2022). Unlike pure time series forecasting, SocioDojo prohibits day trading",
forcing the agent to predict long-term trends that rely on information beyond historical time series data towards stronger form of the efficient market. Time series forecasting has also been studied in economics (Granger & Newbold, 2014), polls Srewis-Beck, (005). and search trends (Xu & Berkely 2014 Choi & Varian 2012) To make reliable predictions these topics, it is necessary to access a variety of sources of information, which can be found in SocioDojo.
3 SOCIODOJO
In this section, we first formulate the Hyperportfolio task in the Partial-observall Markov Decision Process (POMDP) framework in Section 3.11 Then we introduce the construction of SocioDojo, which serves as the environment for the POMDP in Section 3.2
3.1 HYPERPORTFOLIO TASK
Most existing learning environments are designed for embodied agents that learn to take the right sequence of actions to interact with the environment, which can be smoothly assessed by checking the immediate outcomes of the agent's 's actions. On the contrary, our aim to comprehend and analyze society is not casily quantifiable. People use exams to measure such capabilities, but creating good exam questions necessitates the involvement of experts and regular updates to keep up with the ever-evolving society. We therefore propose a hyperportfolio task, based POMDP, which (1) evaluates agents in scalable manner using the ever-updating real-world time series, and (2) provides a reliable indicator of the level of analytical power and understanding of the real society.
3.1.1 SOCIETY UNDERSTANDING AS DECISION-MAKING
The society can be modeled hidden Markov process P(wy)ss) where 8t is the current hid- den world state, Wi is the observation. Understanding society is to estimate the hidden world state denoted as ((seww)) Humans perceive society through multiple sources of information, such as
3

---

Published as a conference paper at ICLR 2024
newspapers, social media, websites, etc. The world state, the other hand, is portrayed by the time series of different facets of society. For instance, economic and financial time series illustrate production, trade, consumption, activities, etc., search trends uncover the public's interests, and po- litical and opinion poll trackers demonstrate people's views on politics and public issues. These time series were designed to probe the world state as accurately as possible through statistical tech- niques and social science studies. Therefore, we can regard those high-quality time series as a near approximation of the hidden world state. We use probe set Y, E R consisting of the latest values of a set of time series at time t, where N is the number of selected time series, as the approximate current world state. Consequently, estimating the probe set can be used as a substitute task for the estimation of the current hidden-world state, where P(ss|ux) R ((Y](u>).
However, is not possible to accurately estimate the current probe set given the current observations. Prohe values are delayed, denoted as P(ye|sy) where y/ E Yú and E'< t. Economic statistics and public polls take time, financial data need time to reflect the latest information, and search trends need to wait until topic is broadcast before it becomes spike in the time series. The observations are also delayed, denoted P[ (s(") where Wi E S2 and t" < t. Since news cannot be perfectly real-time due to production and delivery time. Furthermore, newly published message may not necessarily talk about the latest world. As result, the probes and observations are mismatched, and can only approximately estimate the probe value an undetermined future time point.
Inspired by portfolio optimization, we convert the precise estimation problem to a surrogate decision-making task, the hyperportfolio, which largely relies on the estimation power of the world state, i.c. the capacity to understand and analyze society. Modern portfolio theory has been intro- duced in finance to maximize the expected return of combination of financial assets expressed as max E Bpp) = - (R)) where w; is the proportion of the i-th asset in the total investment value, R2 is the return of the asset i, its definition can be complex, simple model is
g -p""""""" buy
where buy is the buying price of an asset, b denotes the buy time, and yلell is the selling price, the price is the value in the time series with buying or selling time.
In hyperportfolio, all high-quality time series that accurately probe the world state, not only financial ones, are investable assets. The current price of an asset is determined by the most recent value in the corresponding time series. An agent needs to make investment decisions over time and maximize its assets to show its analysis power and understanding level of society. As we assume wise investment decisions that maximize the expected return should rely on a good estimation of the dynamics of the world state. To ensure this condition, we design mechanisms that will be discussed later in Scction 3.2.2 based on our robustness analysis in Appendix C to reduce the chance of unwanted speculation activity and the factor of "luck". This task is easily scalable, as time series, information sources, and knowledge bases constantly updated in the real world, which also ensures that the agent developed in SocioDojo can be immediately applied in real life.
3.1.2 POMDP OF HYPERPORTFOLIO
A hyperportfolio H = {(c,, w; where Cs E C' is the ID of an asset, in SocioDojo, each time series has unique II) C' is the set of all IDs, and w, is the ratio of this asset in the portfolio. For convenience, we consider cash (co. Wo) an asset in hyperportfolio whose values do not change over time for simplicity. The hyperportfolio for each agent will be initialized as cash only, which means Ho = (Co, 1). We formulate the hyperportfolio task in POMDP
(1)
where S is the hidden state space, Q is the observation space, A is the action space contains the hyperportfolio adjustments (c,, A), > O, where -1 < A S 1 is the ratio to the total asset, the sum of cash and the portfolio value. A positive À indicates "buy", which requires Q: E C, and X S wo to ensure there is sufficient cash. A negative À represents "sell", which requires G; E CH, the holding assets, and X; < W;, where the sell value should not exceed the owned value. = means no action. We do not introduce some financial actions like leverage and short for simplicity. The reward R= action. T is the conditional state transition function, where actions do not impact the transition of the world state which is driven by the human society itself. O is the conditional observation function; similarly, assume that ( which is
- E(R,, R2.) is the change in the expected returns of the hyperportfolio after taking
assume T(s'+1|s' 1 a²) T(s*+1 'ss")
4

---

Published as a conference paper at ICLR 2024
driven by the media in the real world, is independent of the action. The discount factor is since we do not care about historical improvements to the hyperportfolio. Additionally, context Z is introduced, as the observation may not contain the necessary context for analysis.
3.2 SOCiODorO ENVIRONMENT
SocioDojo uses three components Information Sources, Time series, and Knowledge base & Tools based on ~30 GB of high-quality real-world data that we have collected, to create a simulated environment for POMDP in equation [ With these building blocks, two key modules, the World Runner and the Broker, are used to implement the work process of the hyperportfolio POMDP. Details about how reduce biases and filter sources can be found in the Appendix
3.2.1 COMPONENTS
Information Sources. This component composed the observation space Ω with high-quality real- world media collected from the Internet and manually filtered to reduce noise and bias between October 1, 2021 (the date of the most recent knowledge of GPT-3.5 and GPT-4) and August 1, 2023. Sources cover various types of information, such as newspapers, social media, public reports and insights, research papers. website articles, etc. The distribution of types and topies for each type can be found in Table | and Appendix [A.1 re.rreettcl.lly. while Appendix A.2 shows the distribu- tion of topics of messages over time. The information sources were stored in unified structure (index, metadata messages) called channels which include the datetime, metadata, and contents the messages in each channel, respectively. Agents can customize information sources queue of messages (date: et sime, met adaaadaa, content), ascending ordered by time, with channels. We follow the terms of use to curate our data, and for the few copyrighted contents that did not allow distribution, we provide metadata including the content URLs to minimize the effort for researchers to rebuild the environment in their local settings.
Type
Num
Avg. Tok.
Avg. Per day
Newspapers
175.2K
674
262
Social media
259.8K
50
388
Google
Reports
19.5K
4926
15 4%
(102171
Papers 6.2K
26939
Pol Track
Other
32.7K
1596
49
(1634)
Table 1: Statistics of information sources by type.
44 9%
(13393)
Type
Keys
Tokens
Avg. Tok.
Encyclopedia
57.4K
14.8M
258
Finance
Books
167.5K
41.0M
245
Papers
1.7M
392.4M
228
Figure 2: Statistics of time series by topic. Num- ber of time series shown in the bracket.
Table 2: Statistics of knowledge base by type.
Real-world Time Series. This component provides the probe set Y discussed in Section 3.1.I to approximate the world state S. We collect time series variety of topics for comprehensive probe of the world state, including financial data from Yahoo Finance) economic time series from the St. Louis Federal Reserve Economic Data Database (FRED) with popularity rating of more than 50%, Google trends free trending keywords from Exploding Topics a society trend tracking service, political polls from Five ThirtyEiihh famous political analysis website, and public opinion poll trackers from YouGov an online survey platform. We remove any time series that contain nonpositive values. We also rescale Google trends with random number from 0.1 to 10, since Google trends use relative scale from 0 to 100 which could potentially leak future information to the agent. The distribution of topics shown in Figure and is detailed in the Appendix A.3
Knowledge Base & Tools. This component complements the agent with the necessary context Z that is not available from the input messages. Open resources on the Internet such as Fincyclopedial Concise Encyclopedia of Economies Ballotpedia textbooks, and journal articles economies,
5

---

Published as a conference paper at ICLR 2024
finance, business, society, etc. used to build the knowledge base. The texts segmented, randomly shuffled, and ready for the retrieval-based knowledge base to avoid the distribution of original copies. The toolbox includes search engines and knowledge graph interfaces, which filter search results by current time during the hyperportfolio task to avoid leaking future information. The statistics of the knowledge base can be found in Table and and further detailed in Appendix A.4
3.2.2
WORK PROCESS
World Runner. World Runner consistently pops the messages from the message queue discussed above. When message pops out, the world runner sets the world time to the published time of the message and sends it to the agent. The agent runs its internal loop, which involves access to the knowledge base, historical time series, and tools, and interacts with the Broker module send its trading instructions. The world runner pops all messages up to the end time, i.c. Aug. 1, 2023, from the queue and then calculates the hyperportfolio returns at the end time as the result.
The broker manages the "account" for the agent, executes the trading instructions, and returns the results, including potential errors, to the agent. We introduce 4 mechanisms to alleviate undesired returns: Forbid day trading, Commission fee, Overnight fee, and Return bound. The detailed definition of these mechanisms, well as our study of the volatilities of time series and random walk tests to find the optimal overnight rate and bound, can be found in the Appendix C
Broker.
4 AGENT ARCHITECTURE AND HYPOTHESIS & PROOF PROMPTING
We present our Amalyst-AssistattAAttuaa (AAA) agent architecture tackle the hyperportfolio task in Section 4.1 and an overview can be found in Appendix B Subsequently, we discuss the
Hypothesis & Proof prompting technique for generating high-quality analysis in Section 4.2
4.1 ANALYST-ASSISTANT-AAAA ACTUATOO ARCHITECTURE
We break down the analysis and decision-makinn process into three distinct roles with varying duties and access to the three components of SocioDojo: analyst, assistant, and actuator. To (1) save tokens for each role, (2) have each role concentrate on a particular task with a precise context, which could enhance the precision of the subtasks, and (3) provide better flexibility, we can different foundation models for different roles to save cost, we do an ablation study for this in Section 5.3
Analyst. The analyst is the entry for messages. First, it reads the metadata, which may include the title, description, and category, to determine whether necessary to read the full content or not to save costs. If it is decided to read the full content, the analysis loop, multi-round dialogue between the analyst and the assistant, will begin. The analyst can use the ask function to request the assistant to seek information to complete the analysis and the done function to finish the analysis. The system will then prompt the analyst to provide a final analysis report. Finally, the analyst can decide whether send the report to the actuator not, depending whether there is any immediate useful information for the actuator to adjust the hyperportfolio. The user can specify a maximum number of steps beyond which the agent must provide its final report to balance cost and performance. In our experiment, we set this number to 4.
Assistant. The assistant has access to the knowledge base, tools, and historical time series. It answers queries ranging from simple data requests more complex questions such as "Will some event impact the economy?". The query is handled iteratively through a multi-round dialog response loop with a max step of in our experiment. at each step. The knowledge base and the time series base are implemented as two vector databases by ChromaDB with Instructor-XL. XL (Su et al. 2023) as the embedding model. The natural language query is embedded by the model, then its inner product distance is computed to the embeddings of snippets stored in the database, and the top candidates are returned. For the knowledge base, the snippets are segmented texts discussed in Section 3.2.1 for the time series, the snippet the code of a time series and its description, allowing the agent to find the code of a desired time series. When the assistant gets sufficient information, can call the done function to return the final answer.
can call one of the query interfaces to find resources

---

Published as a conference paper at ICLR 2024
Actuator. The actuator interacts with the broker. It initiates a multi-round dialog action loop with a max step of in our experiment when an analysis report is received. It can query the assistant or send trading instructions, which written in multiline script. Each line of the script follows the format BUY /SE/LL [CODE] [VALUE], and the done function is called to complete the loop.
4.2 HYPOTHESIS & PROOF PROMPTING
Existing prompting techniques optimize reasoning in scenarios where the query can broken down into clear procedures. For example, Self-Ask (Press et al. 2023) encourages the agent to ask ques- tions to collect information during reasoning, while Plan & Solve (Wang et al. 2023a) guides the agent devise plan to address the problem. Such iterative approach encourages more thor- ough reasoning process. Nevertheless, the analysis process cannot be partitioned into distinct steps, and it is not clear what questions can enhance the depth of analysis which can extrapolate more useful indicators that can direct investment decisions in the context of hyperportfolio; moreover, it hard to devise samples that prompt the agent in few-shot way like most existing methods.
Inspired by the scientific research process, we propose the zero-shot Hypothesis & Proof (H&P) prompting. This meta-thinking framework encourages the agent to come up with hypotheses beyond the domain of the input and to search for evidence to prove or disprove them. The prompting is provided as additional context for the analyst, who will make hypotheses, such as "cold weather will reduce people's willingness to shop". and ask the assistant to find evidence to support or refute it. The analyst will then decide whether to accept or reject the hypothesis based on the evidence returned. If there is no sufficient support, the analyst can choose to keep it as an intuition. In the final report, the analyst will clearly list the hypotheses, proofs, intuitions, and references to the evidence. Detailed promptings can be found in the Appendix
We provide an example Appendix G The analyst can iteratively propose more in-depth hypotheses that gradually uncover deeper insights from the input step by step. This allows the analyst to be more sensitive and notice subtle trends that may be overlooked by other analysts who only ask fact-search questions. We evaluate them and discuss the results in Section 5.2
full H&P analysis process and compare it with the P&S prompting in
EXPERIMENT
In Section 5.1
we present our experimental setup. We then evaluate our proposed H&P prompting and compare it with other state-of-the prompting techniques in Section 5.2 Finally, we discuss the results of the ablation studies in Section 5.3
5.1 EXPERIMENT SETTING
We designed two experiment settings. Strandard allows the agent to freely configure its hyper- portfolio and Trucking, which limits the assets the hyperportfolio to a list of 100 selected time series, including the Dow Jones 30 and other popular financial assets, 20 most popular time series in FRED, national polls, and a few Google Trend keywords, to evaluate the ability of agents to comprehensively analyze the major trends of society. The list is provided with the dataset.
We compare H&P with the following state-of-the-art methods. All methods use AAA architecture, one news channel the information source, GPT-3. 5-Turbo series foundation models with low temprature=0 2 for a more deterministic experiment result.
Plan & Solve (P&S) (Wang al. 2023a) guides the agent to design plan first and then execute it to solve a problem. P&S is zero- zero-shot, so we consider as a major baseline and utilize it in the analyst by replacing "answering the question" with "analyzing the article" in the prompt.
Self-Ask (Press et al. 2023) is a few-shot prompt that leads the agent to ask questions to search during question-ansse answering. We use it with assistant as few-shot samples for the analyst unclear.
ReAct (Yao et al. 2023c) allows the agent to refine its actions by observing the results of previous activities, such searching or using tools. ReAct is also few-shot, thus employ it in the assistant.

---

Published as a conference paper at ICLR 2024
H&P (Ours)
Vanilla
P&ST
Self-Ask2
ReAct
AutoGPT
full
w/o AAA
w/ Ask
w/o Ask
Fin
15.3
13.0
12.4
11.8
10.9
9.8
10.4
6.
Econ
5.5
4.5
1.0
3.6
2.2
-6.3
1.8
0.2
Web
15.0
13.8
11.1
9.4
9.5
8.3
10.2
-4.5
S.
Poll
3.6
2.2
4.1
-2.9
1.7
3.1
-1.2
2.5
All
14.3
11.9
10.8
9.9
9.4
8.9
9.8
3.1
S
3.21
2.91
2.90
2.60
2.15
2.77
2.16
1.20
Fin
15.6
13.9
12.0
11.6
10.3
8.6
10.7
8.0
Econ
2.8
-4.2
-3.2
-1.1
-1.7
1.0
N/A
0.2
Web
13.7
14.9
8.7
4.3
4.9
13.3
3.8
7.3
T.
Poll
3.1
2.7
N/A
1.4
-1.1
2.3
0.1
-6.7
All
15.0
13.1
11.5
10.6
8.8
10.3
10.0
6,6
S
3.07
2.42
2.68
2.15
2.43
2.45
2.35
1.67
Table 3: Percentage returns in hyperportfolio task of agents including ours, 1. Wang et al. 2023a) 2. Press et al. (2023), 3. Yao al. (2023c), |Rihaadds] (2023), and vanilla analyst without additional prompting. "S." and "T." represent "Standard" and "Tracking" settings respectively. "AAA" means Analyst-Assistant Asctuttttst architecture. "Ask" means whether the agent can access the Internet and knowledge base. "All" means the overall return, and "Fin", "Econ", "Web", "Poll" denotes the returns the assets in those topics. "N/A" means the agent does not invest assets in that topic. "S" percentage Sharpe ratio, reference, S&P 500 in the same period is 1.53, Apple stock is 4.73.
AutoGPT (Richards) 2023) is the state-of-the-at LLM agent framework that encapsulates tools and prompts that allow automatic planning and completion of given task, which is analysis in our case.
We also tested our method without the AAA architecture, in which a single agent plays all roles, vanilla analyst without any extra prompting and one without access to the knowledge base & tools.
5.2 EVALUATING AGENTS
Table the results. Our H&P prompting had the highest returns with 14.3% for the standard setting and 15.0% for the tracking setting, which is 32.4% and 30.4% improvement over the P&S, the second-best performing method. H&P also outperforms most of the topic-wise returns. We also provide the Sharpe ratio measure risk-adjusted returns by S (o where rp and op are the oe
mean and standard deviation of daily percentage change in the hyperportfolio value, and ryy is the risk-free return, which is assumed to be 0 in our setting (see Appendix C). It is important to note that SojioDojo uses publicly available daily close data for financial assets, which cannot be directly interpreted compared to real financial returns. We discuss the potential impact of this and the returns adjusted by the commercial minute-level financial data from Vantage in Appendix D
Both H&P and P&S benefited from augmenting the analyst, which can be attributed to their iterative approach. H&P fur- ther encourages the agent to explore more deeply by forming out-of-domain hypotheses. For example, H&P learns to com- pare similar situations history to discover potential indica- tors that are not directly available from the input, as shown in Appendix G. contrast, P&S does not ask questions outside of the input. Specifically, H&P analyst is likely to ask ques- tions such as "what the potential impact of on Y?" or "what is the significance of X in Y?", while baselines tend to ask questions such as "what is X?". The statistics in Table demonstrate that our H&P agent made a significantly higher number of valid ask calls, indicating that able to form more in in-depth hypotheses. In contrast, the P&S agent only had slight increase in ask calls, likely due to its inability to construct useful questions. Other baselines augment the assistant. How- ever, the analyst's lack of in-depth questioning and early termination of the analysis prevented the
H&P
P&S
Other
Mean Std.
1.91
0.72
0.52
0.98
0.88
0.85
Table 4: Number of ask calls made by agents in our experiments.
8

---

Published as a conference paper at ICLR 2024
improvement from being demonstrated. Furthermore, the vanilla agent without access to the knowl- edge base and tools performs poorly due to the lack the latest background of the current world, for example, the world before and after COVID is largely different. Thus, conclude that the analyst plays a key role in making high-quality plans and utilizing the assistant more effectively.
5.3
ABLATION STUDY
Type
Fin
Econ
Web
Poll
All
Model
Fin
Econ
Web
Poll
All
News.
15.3
5.5
15.0
3.6
14.3
GPT-3.5
15.3
5.5
15.0
3.6
14.3
GPT-4
17.2
7.3
16.7
4.2
16.8
+Social.
14.3
5.3
15.3
2.3
14.1
+Report
15.9
6.3
14.2
3.9
14.7
Assis.
15.9
5.8
16.4
4.3
15.2
Papers
15.3
6.7
15.0
3.4
14.6
Actua.
15.8
6.0
15.3
3.1
14.9
Other
15.0
5.3
14.9
4.9
14.4
Analy.
16.9
6.8
16.2
4.4
15.9
Mixed
15.5
5.8
15.4
4.0
14.7
Table 6: Ablation study on foundation models by replacing "GPT-4" for all roles or for each of "As- sistant", "Actuator", "Analyst".
Table Ablation study of types of sources.
Information sources. We add additional sources of different types the basic setting above. The topic distribution of each type can be found in the Appendix A Table 5.3 shows the results, which indicate that the inclusion of more information sources does not necessarily lead to better perfor- mance. This could be due to the fact that different sources require different analysis techniques; for instance, reading papers is different from reading news, and some messages, such papers, were truncated to fit the token length limit. Furthermore, information sources may also introduce noise, such as fake news unverified opinions in social media, even though we have filtered relatively reliable publishers. Therefore, we conclude that high-quality information sources are essential, and domain-specific analysis techniques are necessary.
Foundation models. We substitute the foundation model with GPT-4 for all roles or for each role. The results are presented in Table We can observe a marked improvement over GPT-4, which can be attributed to its more comprehensive knowledge of social science, evaluated by multiple human tests (OpenAl 2023). We also note that the most significant improvement is seen in the analyst, which is the main bottleneck. Enhancing the actuator also yields an improvement, though not as prominent, indicating that the bottleneck does not come from the actuator. In contrast baselines, improving the assistant gives boost, which can be explained by the fact that the questions asked by a H&P analyst are more complex and require more capability. Therefore, we conclude that the analyst is critical for the hyperportfolio task and also has an effect on the gains from the assistant, while well-executed actuator is also beneficial but not the highest priority.
6 CONCLUSION
In this paper, we introduce SocioDojo, a lifelong learning platform for developing ready-to-depley agents that are capable of analyzing and making decisions about the dynamics human society. SocioDojo is composed of real-world news, articles, papers, etc. over time from diverse information sources, time series from economics, finance, search trends, and polls that support the hyperportfo- lio, a decision-making making task that can evaluate agent analysis and decision-making power in reliable and scalable way, and a knowledge base of books, journals, encyclopedias along with the search interfaces. Our suallyt-Assistan--AAtuuae improvements of 32.4% and 30.4% in two experiment settings compared to the state-of-the--e aat meth- ods, which is promising start for the development of lifelong analytical agents for human society that aim to achieve human-centered AI.
architecture and Hypothesis & Proof prompting achieve
7 REPRODUCIBILITY STATEMENT
We ensure the reproducibility and completeness of this paper by introducing the hyperparameters and experiment settings in the main paper. To make the results more deterministic and reproducible,
9

---

Published as a conference paper at ICLR 2024
low temperature for LLMs. We also design mechanisms make the results stable and provide details including two key hyperparameters, overnight rate, and return bound in the Appendix Furthermore, provide all the promptings for architecture and details about the analysis process in Appendix F To enable readers to reproduce our work, we make our code and data publicly available.
REFERENCES
R. K. Branavan, David Silver, and Regina Barzilay. Non-linear monte-carlo search in civilization ii. In Proceedings of the Twenty-Second International Joint Conference Artificial Intelligence Volume Volume Three, IJCAI' 11, pp. 2404-2410. AAAI Press, 2011. ISBN 1781577353151.
Po-Lin Chen and Cheng-Shang Chang. Interact: Exploring the potentials of chatgpt as a cooperative agent, 2023.
Hyunyoung Choi and Hal Varian. Predicting the present with google trends. Economic record, 88: 2-9, 2012.
Eugene F. Fama. Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2)383-411,, 1970. ISSN 00221082, 15406261. URL huLLLttp:/// /www jstor. org/ stggstaabee22224555
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang. Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar. Minedojo: Building open-ended em- bodied agents with internet-scale knowledge. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022. URL https::/openreviee
n htt:/forumm?ici Ff-ruumiddrrc__111 _818P
Clive William John Granger and Paul Newbold. Forecasting economic time series. Academic press, 2014.
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell. The malmo platform for artificial intelligence experimentation. In Proceedings of the Twenty-Fifth International Joint Conference Artificial Intelligence, IJCAT' pp. 4246-4247. AAAI Press, 2016. ISBN 9781577357704.
Bryan T Kelly and Dacheng Xiu. Financial machine learning. Technical report, National Bureau of Economic Research, 2023.
Kyoung-jae Kim. Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2):307-319, 2003.
Bjoern Krollner, Bruce Vanstone, and Gavin Finnie. Financial time series forecasting with machine learning techniques: A survey. In European Symposium on Artificial Neural Networks: Compu- tational Intelligence and Machine Learning, pp. 25-30, 2010.
Michael Lewis-Beck. Election forecasting: Principles and practice. The British Journal of Politics and International Relations, 7(2):145-164, 2005.
Alejandro Lopez-Lira and Yuehua Tang. Can chatgpt forecast stock price movements? return pre- dictability and large language models. arXiv preprint arXiv:2304.07611 2023.
Mantas Lukauskas, Vaida Pilinkiené, Jurgita Bruneckiené, Alina Stundžiené, Andrius Grybauskas, and Tomas Ruzgas. Economic activity forecasting based on the sentiment analysis of news. Mathematics, 10(19), 2022. ISSN 2227-7390. doi: 10.3390/math111194611 URL https:
/ 7/www. mdpi com/ 729077730/1/// /1/1/4611
OpenAl. Gpt-4 technical report, 2023.
Ofir Press, Muru Zhang, Sewon Min. Ludwig Schmidt, Noah A. Smith, and Mike Lewis. Mea- suring and narrowing the compositionality gap in language models, 2023. URL https:
7/openrevi ew. nwt.ffftooooo www.:/tttw///i/eeeoo 0=P
10

---

Published as a conference paper at ICLR 2024
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Tor- ralba. Virtualhome: Simulating household activities via programs. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 8494-8502, 2018.
Chen Qian, Xin Cong, Wei Liu, Cheng Yang, Weize Chen, Yusheng Su, Yufan Dang, Jiahao Li, Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. Communicative agents for software de- velopment, 2023.
Toran Bruce Richards, Autogpt - the next evolution of data driven chat ai, Mar 2023. URL https: //auto-gpt. ai7
Omer Berat Sezer, Mehmet Ugur Gudelek, and Ahmet Murat Ozbayoglu. Financial time series fore- casting with deep learning: A systematic literature review: 2005-2019. Applied soft computing, 90:106181, 2020.
Rohin Shah, Cody Wild, Steven H Wang, Neel Alex, Brandon Houghton, William Guss, Sharada Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, et al. The minerl basalt competi- tion on learning from human feedback. arXiv preprint arXiv:2107.01100 2021.
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning, 2023.
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. Alfred: A benchmark for interpreting grounded instructions for everyday tasks. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 10740-10749, 2020.
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. ALFWorld: Aligning Text and Embodied Environments for Interactive Learning. In Proceedings of the International Conference Learning Representations (ICLR), 2021. URL https:
arg/abs/////ooooooorro..ra/.aaor abs 2010 03768
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. One embedder, any task: Instruction- finetuned text embeddings. In Findings of the Association for Computational Linguistics: ACL 2023, pp. 1102-1121, Toronto, Canada, July 2023. Association for Computational Linguis- tics. doi: 10. 10.18653/v1/2022 htt.////////222.mmnnnmaa.....7/ URL https: waccannthol.. logy. org/2023. Eindings-ac1
Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, and Doina Precup. AndroidEnv: A reinforcement leam- ing platform for android. abs/2105.132211, 2021. URL httt:://axiv..rrt www.xii..rg/aa///////
13231
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, An- ders Ekermo, Jacob Repp, and Rodney Tsing. Starcraft ii: A new challenge for reinforcement learning. 2017.
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large lan- guage models. In Proceedings of the 61st Annual Meeting of the Association for Computa- tional Linguistics (Volume Long Papers), pp. 2609-2634, Toronto, Canada, July 2023a. As- sociation for Computational Linguistics. doi: 11665///1/222.ac//llelo 147. URL https:
//aclanthology
acc.ooooo..... 147
Xuezhi Wang. Jason Wei, Dale Schuurmans, Quoc Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In The Eleventh International Conference on Learning Representations, 2023b. URL
http:s//caaaanneeeeeeeeeeeerrerr net dttt:
11

---

Published as a conference paper at ICLR 2024
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35:24824-24877 2022.
Frank Z Xing, Erik Cambria, and Roy E Welsch. Natural language based financial forecasting:
Survey. Artificial Intelligence Review, 50(1):49-73, 2018.
Selene Yue Xu and CU Berkely. Stock price forecasting using information from yahoo finance and google trend. UC Brekley, 2014.
Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. Webshop: Towards scalable real-world web interaction with grounded language agents, 2023a.
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of Thoughts: Deliberate problem solving with large language models, 2023b.
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du Izhak Shafran, Karthik R Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting language models. The Eleventh International Con- ference on Learning Representations, 2023c. URL https: opperevieew....mmmmooe net. forum?
Id=WE_vluYU1-
Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig. Webarena: A realistic web environ- ment for building autonomous agents, 2023.
12

---

Published as a conference paper at ICLR 2024
A SOCIODoJO STATISTICS
A.1 DISTRIBUTION OF TOPICS FOR BACH TYPE OF INFORMATION SOURCE
Reports: 96.0M
Papers: 165.7M
Society Other
Finance
Economy
6.9%
3,7
(6.MM
(14.44M)
(75.0M)
96. 3%
(159.6M)
Finance
Economy
Twitter: 12.90
Other: 52.2M
Society & Politics
Politics
Other
(M)
18. 13.1%
(2.3M)
(6. (6.8M)
Finance
21 4% (M)
Economy
Society
Figure 3: Distribution of topics for each type of information source by number of tokens.
A.2 DISTRIBUTION TOPICS FOR INFORMATION SOURCES OVER TIME
Number of d florene types of messages over time
6230
5230
Shance
4330
3330
1220
2012
Figure 4: The x-axis indicates the date, and each bar represents the distribution of the number of messages for each topic over a 14-day period beginning with that data. The graph displays how the topics of messages have been distributed over time in all information sources.
13

---

Published as a conference paper at ICLR 2024
A.3 DISTRIBUTION TOPICS TIME SERIES FOR EACH DOMAIN
Finance: 13394 (44.87%)
Economics: 4605 (15.43%)
Money, Benking. Finance
CTF
Internatioal
Regional Data
(2992)
(574)
12.0%
OTC
(252)
29.8% (3987)
0 1903)
Other
(586)
7.9%
Activity
3.0%
(649)
(167)
Others
41 1%
18.4% (849)
(860)
Population, Employment, & Lebor Markets
Stock
Poll Track: 1634 (5.47%)
Google Trend: 10216 (34.22%)
Startup
Political
Product
Concapt
34 5%
(562)
13.1%
(805)7.38
8 7%
(744)
Software
5.3%
Technology
8% (1716)
Health
(384)
65.5% (1071)
(1821)
17. 17.8%
Company
Others
Public
Figure 5: Distribution of topics in different domains of time series by number of time series.
A.4 DISTRIBUTION OF TOPICS FOR KNOWLEDGE BASE SOURCES
Encyclapedie:
Papers:
Pollics
Society
Political Science
2 2429 1M
8.1% SM SRI
128
Other
15.1% (59.1M)
43.5%,
42.0%
Economyy
90.4% (13.4M)
29.00 (M)
(25.50)
Anance
Finance
Fremonmy
Figure Distribution of topies for each knowledge base source by number of tokens.
B METHODS IN CREATING SOCIODOJO
News media have political biases, to reduce the influence of these biases on our information sources, we refer to the Media Bias Ratings and Charts from Allsides) which provides a rating from -6 to 6. We only choose media with ratings between -3 and 3, and as close to neutral possible. We also balance the ratings by letting the summation of the ratings all selected media be as close to O. Additionally, refer the media rank from Stony Brook University to pick highly ranked
14

---

Published as a conference paper at ICLR 2024
media. We use Google Scholar venue rankings to screen for reliable sources of academic papers. We filter social media accounts by the number of followers and then manually remove accounts that show clear biases. We manually search for reputable institutions in different domains to obtain their publicly available reports. We also select texthooks from the Internet that have good ratings. To prevent information leakage, the materials in the knowledge hase are published before the start date of the environment 2021-10-01, and we assume that the knowledge base information is timeless and valid throughout the experiment period we manually pick only the highest-rated sources from the Internet. Finally, we also consulted experts and librarians in economics and other domains to verify the reliability and diversity of our applied sources.
C ROBUSTNESS ANALYSIS OF SOCIODOJO
Robustness is used to describe the ability of a system to withstand disturbances that could potentially affect its functioning. In the context of SocioDojo, which is responsible for assessing the societal analytical and decision-making capabilities of agents, robustness can be determined by whether an agent can achieve a high return without relying on high-quality analysis and sound decisions.
Moun (1 days!
"oilillyy days!
Miaan (2 days!
Volatily days)
3.-
545
31-
-11
VdP
W0D
Domain Valaniityy days)
Domalls Mean (00 dayst
Mean 130 dayst
Valastitty daysi
0.5
3.09
0:0
6:09
AdL
-0.4
Oannen 1.80
Uei days)
C355
V17
Damann
Deman
Deman'
Figure 7: The volatility and mean changes under different sampling periods for time series in differ- sources. 0.1 on the y-axis means 10% return.
We introduce four mechanisms to alleviate undesired returns: (1) Forbid day trading, that an asset cannot be sold within 5 days of purchase to avoid profiting from short-term patterns. Moreover, balances the agent's 's information advantage as we use close prices in finance which do not reflect the latest news real-time. (2) Commission fee, charged for each transaction to avoid frequent trading and encourage considered decisions. We use I % of the trading value in our experiments. (3) Overnight fee, calculated as rate X size x y² /360 where size = Ubase/ h"yy and Ubase are the cash used to buy the asset, charged daily for some assets. Since some assets are almost wver-growing, such as GDP, which leads to risk-free returns, we wish all returns to come with risk, thus requiring good analysis to achieve better risk-return trade-off. We use 0.1 for economics and 0.05 for search
15

---

Published as a conference paper at ICLR 2024
trends. (4) Return bound, limited the returns of an asset by -bound Ubase I R, S bound X Vbases thus avoiding extreme value and alleviating speculation from high volatility, We use a bound in setting.
We begin by studying the volatility of the time series from various sources, as seen in Figure 7 FIN stands for finance, FRD is the economic time series from the FRED database, YGV is the poll trackers from YouGov, and FTE is the political poll data from Five TivettityEighh, The "Poll" type in the SocioDojo time series is a combination of the YGV and FTE time series. WEB is Google Trends.
Volatility is measure of the standard deviation of changes in time series with given sam- pling period, such as the standard deviation of the weekly change. It is calculated as dp = V NF Ex (u. Up)2, where do is the first day and d, is the last day of the time series, P is the sampling period, of = Vs+P
is the change in value during a period of length beginning on the day i, NN is the number of spans under the period of this time series and vp = Σ P of the mean change under the sampling period P. High volatility usually indicates a high risk, while high mean usually implies a high potential return.
Ne
FRD shows high means for all its quartiles as some FRD time series, such as GDP and population, are almost weer-growing which presents both a risk robustness that agent may randomly invest in an economic time series and still get good return without conducting thorough analysis. This has motivated speculation. We also introduce an overnight rate for the WEB since its high volatility leaves potential risk of speculation. However, the excessive volatility of FTE makes too high risk, and YGV has low volatility and return. Thus, we impose negative overnight rates of 0.05 on them rebalance risk and returns. We also do not apply overnight rates on FIN to align with the real financial market.
introduce overnight rate balance the risk-reward trade-off and reduce
FAD: 8.50, WEB: 0.05
0.10, WEB: 0.05
FRD: WEB 6.65
FRO. 0.20 WES: 0.05
0.27
DLED
0.
-33
0.00
0.1., WED:
FRD:: 0.15 0.30
FR3: 0.30
00-3
1.0
3.0-
2.5
800
-1.3
-1.0
-3.0
Doman
Comaan
Figure 8: Distribution of returns of random walk agent under different overnight rates for FRD and WEB, the returns are truncated around -/+ 120%, 0.1 in y-axis means 10% return. 100 runs for each setting.
The level of robustness for SocioDojo can be depicted by E(Ru), the expected return of unexpected behaviors. We use the random walk agent whose expected return should ideally be 0 to explore the robustness of SocioDojo and find suitable overnight rate. The agent sampled evenly from all assets to buy with a value 5000 to 50,000 and from owned assets to sell with a value of 0 to the owned value in each step and had a probability of 0.5 to make move. The random agent accepted the same information source as the one used in the agent evaluation experiment in Section [52] although the message not actually read, to maintain the same action frequency. Different overnight rates
16

---

Published as a conference paper at ICLR 2024
of FRD and WEB were tested, and each setting run 100 times. The distribution of the returns is visualized Figure
The WEB, FRD, and FIN assets display many outliers, boundary would significantly reduce the likelihood that the agent will receive a high reward by chance. FRD: O. 05, WEB: 0.05, and FRD: 0 10, WEB: 0 05 achieve ideal mean return of O. We also observe that the returns of FTE and YGV have a relatively high variation; however, due to the negative overnight rate, they also have relatively higher mean return. The FIN has relatively high risk and low mean return; this is because the FIN includes all kinds of financial assets, including high-risk assets such cryptocurrency and pink stocks. Nevertheless, the nonrandom agents tend to invest more mainstream companies due to their greater presence in the information sources in practice; thus the return has large difference compared to a random-wall walk agent that evenly samples from the assets.
1e6
Bound
8061 vallu
2:03
V2
0.06
1,33
0.50
upp
0.04
0.30
0.05
0.10
0.10
0.15
1e6
Bound
126 value
0-27
1.23
0.
0.8
077
0.4
0.10
0.13
0.20
3.c0
Bound
1.75
g
1.50
8
1.00
bg
0.30
0.55
0.00
0.0b, 0.05
0.05, 0.10
0.25, 0.13
0.05,
0.10
0.33
0.20
Ovamiggtt ratis
Figure 9: Distribution of final total value of random walk agent under different bound and overnight rates for WEB and FRD. The x-axis shows the overnight rates for WEB, FRD. 100 runs for each setting. Each run starts with 1e6 cash.
further visualize the total return under different overnight rates and bounds in Figure 9 We can that lower bound results in fewer outliers. We chose bound of 5, as it is possible achieve a return of 5 times for a single asset without leverage in around 2 years, which is the time span of SocioDojo. A bound of 10 is less robust and is strict. The overnight setting FRD: 0. 0.10, WFR: 0.05 05 shows a mean final value close to 1 which corresponds to an ideal zero return. Therefore, we selected overnight rates of FRD: 0. 0.10, WEB: 05, and bound of our experimental setting. Although our designs already provide good robustness, a better overnight rate model based on advanced pricing theories can further enhance the robustness, and factors such as dividends should also be taken into account. We leave them for future work.
D ANALYSIS OF FINANCIAL TIME SERIES RESULTS
SocioDojo utilizes publicly accessible daily close price data for financial assets, minute-level historical data usually necessitate commercial licenses to access, and we assess the impact in Section D.I by adjusting the financial returns with minute-level data. We then employ the adjusted data to
17

---

Published as a conference paper at ICLR 2024
delve into the returns obtained by the financial assets to gain more insight into the performance of the agents in Section
D.1 REAL-TIME DATA ADJUSTED RETURNS
H&P
P&S'
Self-Ask2
ReAct
AutoGPT
Vanilla
full
w/o AAA
w/ Ask
w/o Ask
UA
15.3
13.0
12.4
11.8
10.9
9.8
10.4
6.1
A
11.0
10.1
9.7
8.0
7.9
8.4
8.0
3.3
S
2.44
1.95
2.28
2.18
1.91
1.62
1.72
1.09
UA
15.6
13.9
12.0
11.6
10.3
8.6
10.7
8.0
T.
A
11.2
9.9
9.0
9.0
8.2
5.3
7.5
6.6
S
2.36
2.05
2.32
2.19
1.71
1.44
1.80
1.25
Table 7: Percentage returns in financial time series before and after adjusted by minute-level data of agents including ours, 1. Wang et al. (2023a), 2. Press et al. (202)) Yao et (2023)) 4. |Richards 2023), and a vanilla analyst without additional prompting. "S." and "T." represent "Standard" and "Tracking" settings respectively. "AAA" means Analyst-Assisaant-ACite means whether the agent access the Internet and knowledge base. 'UA' means not adjusted, 'A' means adjusted. "S" means the percentage Sharpe ratio of adjusted returns, as a reference, S&P 500 in the same period is 1.53, Apple stock 4.73.
architecture. "Ask"
In order make SocioDojo available to the public, we have chosen to use the daily close price data for financial assets. This has the risk of giving agents an undesired information advantage, they will be trading with prices that are not up-to-date and do not reflect the latest news. It can understood that the agent is always receiving discounted price. Real-time, minute-level historical financial data is publicly available and requires commercial license to access. To evaluate the impact of using close price, we adjust the returns using minute-level data obtained from Alpha Vantage, a financial modeling platform that provides intra-day data including also before and after market prices, adjusted for historical splits and dividend events. We change the trading price from the latest daily close price the latest minute price for each transaction in financial assets, and then recompute the returns for each agent.
The adjusted results are presented in Table 7 After the adjustment, the performance of the agents generally decreased without the information advantage, yet the comparative advantages among them remained. This implies that the daily close price data may not be ideal for developing financial models, but it is acceptable for our purpose of evaluating the agents' understanding and decision- making in the real world. This is further supported by Figure [0, which demonstrates that overall trends and dynamics of the financial asset value are preserved in the unadjusted return of all assets. With the rule of forbidding day trading, the agents cannot solely make profits through short-term arbitrage, but rather through improvements in their analysis ability. This also suggests that the SocioDojo returns only reflect the agents' improvement in understanding and decision decision-making in the real world, and should not be interpreted real financial returns.
18

---

Published as a conference paper at ICLR 2024
D.2 ANALYSIS OF RETURNS
Value over Lime
1.2
1.0
0.9
0.8
H&P (Retum: 12.0% 5m2.40%1
PSS (Seeum 2023309)
HSP (Return: 23%)
0.6
(Return. 50%
2022
2020.1
7072
7072-07
2022.10
2035-11
2073-04
707307
Figure 10: The value of hyperportfolio overtime in the "Standard"' setting for our method (grey dashed line) and the value of financial assets after adjustment by minute-level data for our method, Plan Solve agent, S&P 500, and FANG+ index trackers. We normalize the initial value of all assets as 1 million for a clear comparison.
Figure 11: The top 5 gainer and loser stocks that made the most profits and losses respectively in the invested financial assets for our method (left) and Plan & Solve (right). The y-axis denotes the percentage contributions to profit and losses for stocks marked in the x-axis respectively.
We visualize the returns of financial assets over time in the "Standard" setting after adjustment for our method, Plan & Solve, and the baselines including S&P 500 and FANG+ (the index that tracks the top 10 largest technology companies that are also the main sources of returns for agents) in Figure TO Compared to our method, which performs deeper analysis, Plan & Solve behaves in a more passive style, highly correlated with the market, as reflected in S&P 500. follows the major trends obtained from the information sources to make decisions using little personal opinions normally generated through an in-depth analysis. On the other hand. H&P agent is more aggressive, which may be analogous to actively managed funds. This is further evidenced by the top 5 gainers and losers for our method and P&S shown in Figure The distribution of the top 5 gainers in H&P is less even than P&S, indicating more aggressive and less distributed investment.
19

---

Published as a conference paper at ICLR 2024
E OVERVIEW OF AMALYST-ASSISTANT-ACCUUATT ARCHITECTURE
System: Current
2021-10-04 1858600000000
arfommtion Tieesuyy Decartment is pet
assistont find Trenial aeststtance
Based on current stalus the account and the enslysia of news.
recent trends ossate marker?
Breeking newe!
actustor: my aralysis report: (Mypatessa 'I The redistibution rental- asssisbe for soe morey highrruedd commun provide immediate relef to struggiing
Bogin Redistribuing Asaisiianca High-
instrccions: BUY FINLUR 500O BUY WEB:RTOON 1000
Analyst
Actustor
Communilie,
hoven't spent 30%
money
potandall Impaat of Treasury Depar mant buting rontallcisistirnoe money Nigh need communtiee?
To analyst:: Basad on the search results, here ara sort snurosa that provica information on recant trends...
essistant: Hisorry of FINURI me
Account statuee Caccount" (acoount mame! Velaulf, faesh:: 937481 'eash ranor: o. soloo, assets valuee: 61096.2
Systemt: cxcouted successiully
To anolyst: on some
WEB:RTOON FINURI (distance
prozid ghih
the potentaall Impact
Assistant
Tools
Time series
To actustor: Here the history of FINUUR: datetime yelue....
N search in Google Ars....
The overview of how the analyst, assistant, and actuator collaborate to analyze and make decisions for one input from SocioDojo.
Figure 12:
F PROMPTING
F.1 CONTEXT OF HYPERPORTFOLIO
The context of the hyperportiolio task is shown below. The context will be input as the first system message to the Analyst and Actuator before their role prompts. The Assistant uses simplified context in its role context that does not contain the detailed rules of the game.
You are helpful assistant working in a team playing a "nyperportfolio" game that aims analyze and predict potential movement in real-life social, political, economic, or financial trends from real-time news and other information sources. The rule of the game is as follows:
1. Every team has initial account of million cash, your team can use this cash to buy sell assets to build hyperportfolio; the target of the game is to maximize the asset, which is the summation of the remaining cash and value an asset you own.
2. A hyperportfolio composed of a set of assets that corresponds time series in different domains from real-life covers financial market close prices, economic time series, Google search trends, and political and public opinion poll trackers.
3. The game begins on 2021-10-01 and ends 2023-08-01, after beginning, the time will move forward, and you will consistently receive real-life news about what is happening the world, newly released opinions from the internet or social network, or reports from research institutes, financial institutions, and so on.
4. Your team may choose to "buy" or "sell" an asset during the game. Each asset correspouds time series; the buy price will be the latest value of the time series at the current time.
5. You need pay a commission when you buy or sell an asset, the amount is about 1% of the buy or sell value.
6. The value of an asset you own will update over time, calculated as (current price/buy (itvee(((ivestuune amount).
7. For example, you may read news about the Apple company performing well for this season, Based your analysis, you may think increase and decide invest 10,000 cash on the Apple stock time series.
good indicator that Apple stock price will
20

---

Published as a conference paper at ICLR 2024
8. Each time series is marked with ICode. The ICode has the format "DOMAIN;(CODE): For example, the Apple company stock price "FIN:AAPL', FIN is the domain, and AAPL is the code. There are five domains, 'FIN', "WEB', 'FTE', "FRD", 'YGV", interpretations for them:
a) FIN: Close price time series of financial instrument including stocks, ETFs, index funds, RETTs, futures, currencies, indices, or cryptocurrencies
b) WEB: The Google trend time series keyword, such as 'Apple', 'iPhone', 'Bitcoin' etc.
c) FTE: Political poll tracking time series, such as the president's approval rating, the generic ballot, etc.
d) FRD: Economic time series, such as GDP, PPI, unemploymen rate, etc.
e) YGV: Public opinion poll tracking time series, such as support for universal health care, how sustainability policies impact Americans' purchase behavior, etc.
9. You may receive or pay overnight interest or fees if you hold an asset overnight, computed rate* rate"size" ccurrent_prrie/33eee price/360, size=amount/ /buu_/uuyeeeeeeee The rate varies for different assets.
F.2 ANALYST
Context of the analyst. It will be input to the analyst agent as the second system message after the hyperportfolio context.
Your role in the group is analyst who watches and analyzes the latest information, such as news, articles, reports, etc.
1. You will work with an actuator who responsible for managing the hyperportfolio for the team and making buy or sell decisions.
2. Your task is to provide high-quality analysis for the actuator so that the actuator make good decisions that optimize the hyperportfolio.
3. You need find any indicator potential movement social, political, economic, or financial trends from the given news.
4. You can give general suggestions, like "Apple stock price will up', 'Tt is time to sell Apple stock'.
5. You also give precise buy sell suggestions if you have confidence that "I think should spend 10,000 on buying the GDP time series".
6. If you cannot see any opportunity, you should also indicate that 'I cannot see any indica- tors', or "I think should wait for now!
Hypothesis & Proof prompting. It will be inputted to the analyst agent as the third system message after the analyst context.
In order to make convincing analysis, you must clearly state your hypothesis and provide evidence to support your hypothesis:
1. your hypothesis cannot be supported by the facts, knowledge, and information you have, you should seek the help of another assistant agent on your team by calling the ask function.
2. You should never make up facts, knowledge, information, if you do not know, you should call the ask function to seek help from the assistant agent.
3. The assistant agent will find evidence support your hypothesis if you call the ask function and send the evidence to you. can also search for general information, facts, and knowledge, as well as information on an ICode.
21

---

Published as a conference paper at ICLR 2024
4. The hypothesis also be rejected by the evidence; in that case, you should give up the hypothesis and construct new one.
5. Your hypothesis may lack support for both acceptance and rejection; in that case, you can choose to insist on it as an intuition, but you should explain why you insist on it, or you can choose to give up it.
6. When you are done, you should call the done function to finish the analysis.
7. You must call function done ask in each step of the analysis.
Here are some lips for you make a hypothesis:
1. A hypothesis is like guess, assumption, or intuition, it is a statement that you think is true, but you do not have enough evidence to support it yet.
2. For example, 'interest increase in technology will cause the technology stock to increase', "sale of lipstick indicales polential drop of the economy', "the president's approval rate will increase if the economy good", etc.
3. The hypothesis can microscopic or macroscopic, it can about specific asset or a general trend.
Now, progressively analyze the given news, article, report, etc. in multi-round dialog between you and the ask function. When you think that you get enough information to give the final analysis report, call the done function end the dialog with the ask function, and aggregate the progress final analysis report when the system asks you to give your final analysis report. You must call one of the done or ask functions in your response.
F.3 ASSISTANT
Context of the assistant. It will be inputted to the assistant agent the first system message without hyperportfolio context.
You are helpful assistant working team playing a game of "lyperrporffolio" that aims analyze and predict the potential movement in real-life social, political, economic, or financial trends from real-time news and other information sources.
Your role in the team is the assistant who helps the analyst find evidence to support or reject the hypothesis search for the facts, knowledge, information, data demanded by the analyst:
You can access the search engine or database.
2. You need understand the demand from the analyst and generate the query for the search engine or database that can best meet the analyst's demand.
3. If the request is hypothesis and you do not get ideal evidence from your search, you may need to reason and decompose it into subhypotheses, and then search for the subhypotheses find evidence to support the original hypothesis.
4. When you are finished, call the done function to complete the analysis.
5. You can give your opinions, but the main role is to give evidence from your search results.
Here are the tips for using the search engine or database:
1. You can use Google search by calling the googlesearch function to find general infor- mation facts.
2. You knowledge about specific entity.
Google Knowledge Graph Search by calling the gkgsearch function find
3. You can use the Wikipedia search by calling the wikisearch function to find knowledge of a term, concept, or topic.
4. You can use the database by calling the askdb function to find theoretical support from encyclopedias, textbooks, and research papers.
22

---

Published as a conference paper at ICLR 2024
5. You can the probe function to find historical time series and related information, the probe function accepts ICode with the format "DOMEEN; for example, the Apple company stock price is 'FIN:AAPL', FIN is the domain, AAPL is the code. The ICode can be queried by calling the query _icdee function, you should never make up an ICode. There are five domains, 'FIN' "WEB', "FTE", "FRD", 'YGV", interpretations for them:
a) FIN: Close price time series of financial instrument including stocks, ETFs, index funds, REITs, futures, currencies, indices, cryptocurrencies.
b) WEB: The Google trend time series of keyword, such "Apple", 'Trump', 'Bitcoin', etc.
<) FTE: Political poll tracking time series, such as the president's approval rating, the generic ballot, etc.
d) FRD: Economic time series, such GDP, PPI, unemployment rate, etc.
e) YGV: Public opinion poll tracking time series, such support for universal health care, how sustainability policies impact Americans' purchase behavior, etc.
F.4 ACTUATOR
Context of the actuator. It will be inputted to the actuator agent as the second system message after the hyperportfolio context.
Your
team the actuator that manages hyperportfolio of the team:
1. You need to use your strategy to adjust and optimize your team's portfolio by making buy, sell, query, or wait for decisions based on the analysis of the latest news and information provided by the analyst, and the current status of the account which includes the holdings of assets and the
cash.
2. order buy or sell assets, you need to the trade function, you should pass the trading instructions to the function:
a) You should write one instruction per line.
b) There are two kinds of instruction: BUY [[Code] [amount], SELL [ICode] [amount], all instructions should follow this format.
c) For example, if you wish to buy 1Code FIN:AAPL with 2000 amount of cash, then sell 8000 amount of FIN:GOOG, the instructions should be: BUY FIN:AAPL, 2000knSELLI FIN:GOOG 8000
3. When you decide buy or sell an asset, you usually first need to query the ICodes of the related asset by calling the query function unless you already know the ICode from the context.
4. The query function will return the ICodes for the best match assets after execution, then you need to decide the amount and pass an 1Code and amount call the trade function.
5. If there content.
assets you want from the query, you query again with different query
6. You need to make your judgments based on the analysis, your action history and account status, and make function calls about what to do next, such as query related assets, buy sell asset, wait.
7. You should always make function call in your response.
8. You can use the probe function obtain historical time series and related information help you make your decision. The probe function accepts 1Code as input, and ICode can be queried by calling the query function. You should never make an TCode.
Here are some tips for making the decision:
1. The asset you buy should have valid ICode you know of from the query.
2. The amount should always be positive.
23

---

Published as a conference paper at ICLR 2024
3. The assel you sell must be the you own, and the amount should be less than its current value.
4. Use your cash wisely, invest more you see good opportunity and you have confidence, invest less when you do not confirm, and learn to leave some cash for future opportmiiless when you see a risk, sell the assets, the higher risk, the more sell.
5. Higher profit opportunities always come with higher risk, which may cause higher loss.
6. Here
empirical principles when you decide the amount to buy sell: a) Usually if the asset value occupies less than 5% of your total asset, is low and low risk.
The asset value that occupies between 5%%25% of your total asset will give you moderate risk.
b)
c) The asset value that occupies between 25%-50% of your total asset will give you high risk.
F.5 SYSTEM MESSAGES
In addition the prompts that tell the agent their role and context, there system messages that guide their work process.
State messages. When the analyst receives the latest news, article, etc., the system will inform current time of the world, for example:
[System] Current time is 2021-10-02 04:01:00++00:00.
The actuator is also informed by the system about the account status including the hyperportfolio details at the beginning and after trading instructions are executed:
[System] Current status of the account: [account status}
The analyst, assistant, and actuator all work loop manner, every step needs to call function, if the agent does not call function, the system will notify it, taking the analyst as an example:
Error handling.
[System] You do call any function. You must call of ask done.
There also other kinds of errors, such the wrong function calling, wrong action (e.g. the value trading instruction negative), there will be a system message similar to:
[System] You caused an error (error message). Please, retry.
Final response. When the analyst calls the done function, the system will prompt the analyst to provide the final full analyst report:
[System] Now, give the final full analysis report the news, article, etc. based on your dialog with the ask function, you should use the returned evidence and information and make explicit references; you should leave a reference section with the sources listed there unless you have no
24

---

Published as a conference paper at ICLR 2024
reference. You should also include summary of the given news, article, elc. at the beginning of your report to give the context of your analysis, at the end of your report, you should give advice to the actuator. You should never make up any facts, evidence, information.
For Hypothesis & Proof analyst, there is an additional sentence in the prompt:
You should clearly state your hypothesis and proof and your intuitions.
When the assistant call done, it will be prompted:
[System] Now, based on the search results, give your final reply to the query from analyst '[query]'. : You must clearly reference the search results returned by the function calls.
End of step. The system will prompt the agent what to do next after successfully executing an action after each step, for example:
[System] Now you stop and wait for news and opportunities, you continue give more buy or sell instructions based on the analysis, current status the account, and your action history.
[System] You may choose to buy an asset from them or sell an asset you own based on the query result, the current status of the account and the analysis, or perform another query, wait.
[System] Now based on the results, accomplish the query. If there is an error such as an incorrect ICode when calling the probe, handle it, and retry. You also perform further search or decompose the query. When you finish, call done function.
G EXAMPLE OF HYPOTHESIS & PROOF ANALYST AND COMPARISON WITH PLAN & SOLVE
We demonstrate the full analysis process of our proposed Hypothesis & Proof (H&P) prompting and the Plan Solve (P&S) prompting. the state-of-the-art prompting technique that can be applied to the analyst. Both methods given the same input shown in G.I The H&P analyst then makes three queries with increasingly complex hypotheses to the assistant, as shown G.2. while the P&S analyst makes only fact-related query presented in G.2.2 Finally, the H&P and P&S
analysts generate the final analysis report as seen G.3. and G.3.2 respectively.
G.1 INPUT MESSAGE
Current time is 2021-10-08 23:49:00++00000, here the latest news:
Category: TECH
Apple Filing Notice of Appeal in Epic Autitrust Case, Looks to Stay In-App In- junction
Headline:
The iPhone maker wants a stay on judge's order open in-app communications that could allow cheaper payment options
Subheadline:
Datetime: 2021-octaber-8 7:49 PM ET
Told a federal judge on Friday it plans to appeal a verdict in its high-profile antitrust case against Epic Games Inc., contest that has brought new attention how big technology
25

---

Published as a conference paper at ICLR 2024
companies manage soltware and applications on their platforms. The iPhone maker also is seeking put on hold an order by U.S. District Judge Yvonne Gonzalez Rogers that requires Apple to loosen some restrictions on how App Store developers seek payments for their online offerings. Epic, the maker of the popular game "Fortnite," " already had filed a notice of appeal to the Sept. 10 verdict issued by the Oakland, Calif., federal court following the 16-day bench trial in May. An Epic spokesworan declined to comment on Apple's plan to appeal. The case put on trial the ways which companies such as Apple control access their users. Lawmakers and regulators in the U.S., European Union and beyond are closely monitoring the issue. Apple faced deadline to file its notice of appeal, routine procedure that doesn't 't include details of its argument against the of 10 claims that the judge ruled Most of Epic's claims were rejected by the judge, including that Apple was an improper monopoly in its control of distribution of software onto the iPhone and its requirement that forces developers to use its in-app payment system. Apple collects what Epic calls an unfair commission of as high as 30%. Apple had argued its rules protected users and its fees were in line with industry norms and fair for the services it provides. Following the original verdict, Apple has cheered the findings as a "resounding victory". Still, it wasn't a complete win. The judge ruled that Apple was wrong to prohibit developers from using in-app and out-of-app communications with users to send them to alternative payment methods. She issued an injunction to force Apple to stop this ban.
Apple wants stay the injunction while it works solution. The company already had agreed to a settlement in another case to allow out-of-app communications, but remains unclear how Apple wants to address in-app communications. "The company understands and respects the Court's concerns regarding communications between developers and consumers, " the company said in court filing. "Apple is carefully working through many complex issues across a global landscape, seeking enhance information flow while protecting both the efficient functioning Some legal observers have said they think an appeal to the Ninth U.S. Circuit Court of Appeals could take Court, the nation's top court. Epic co-founder Tim Sweeney has suggested the process could take five years. Mcanwhile, Apple isn't allowing Epic back on its developer platform until the appeals process is completed. The legal battle emerged in August 2020, when Epic sneaked an in-app payment into its popular "Fortnite" videogame that violated Apple's rules. In response, Apple booted the game from the App Store, and Epic's lawsuit followed. The case gave a unique window into Apple's secretive App Store business, revealing that about one fifth operating income in fiscal 2019 came from its cut of app revenue, according to a Wall Street Journal analysis of court records that were made public as part of the trial. Apple has said that the released figures were flawed and too high; still, it has acknowledged that much of its App Store revenue came from videogames.
the App Store and the security and privacy of Apple's customers".
more, and the case is likely to ultimately go to the U.S. Supreme
Now, give your analysis that help the actuator optimize the hyperportfolio of the team by buying or selling assets for this news.
G.2 ANALYSIS PROCESS
G.2.1 HYPOTHESIS PROOF
The first ask called by the Hypothesis & Proof analyst:
[Analyst] Ask assistant: What is the potential impact of Apple filing notice of appeal in the Epic antitrust case?
[Assistant] Based on the search results, Apple has filed a notice of appeal in the Epic Games antitrust case. The filing of this appeal could potentially delay any changes to the App Store rules that were ordered by the court. According to CNBC, in September, federal judge Yvonne Gonzalez Rogers ruled in favor of Apple for nine out ten counts the antitrust trial brought by Epic Games, the maker of Fortnite (source: [CttC] www..nbb... www..cco.ccc.cccccc.coo.......e. /2021/22//// 10/08/ files--ppeelll----e--e wwwe--picggge---eee caasss-css--hhhe waw.ooooddddllyccaaaaeee a resounding victory for Apple in the case. Bloomberg Law also reports that Epic Games has filed notice of appeal, indicating their intention challenge the court's ruling (source: [Bloomberg Law (https:/ news.. www.:ssbooombbrlla.coo httplaaw..o/m/attnss//// epic-games-v- apple-ruling-atte wattugggnnnitussssl wiw.ruutsst-ttionnntt liligattioonllkkwwaaa))) takeaways)).
This ruling was seen
26

---

Published as a conference paper at ICLR 2024
While there is specific information available about the potential impact of the appeal, it is clear that both Apple and Epic Games are actively involved the legal proceedings and are seeking resolution in their favor. Further monitoring of this case and any subsequent court decisions or actions would provide more insights into the potential impact of Apple filing a notice of appeal in the Epic antitrust case.
[Aualyst] Based on the news that Apple has filed a notice of appeal in the Epic Games antitrust case, indicates that the legal battle between Apple and Epic Games is ongoing and that Apple is seeking to challenge the court's ruling. This development suggests that there may be potential implications for Apple's business practices and the App Store's rules in the future.
As analyst, it is challenging to determine the exact impact of this appeal on Apple's stock price or the overall financial market. However, it is worth noting that legal proceedings and regulatory actions can have significant impact on the performance of companies the technology sector. Investors and market participants may closely monitor the developments in this case to assess the potential risks and opportunities associated with Apple's business.
Given the uncertainty surrounding the outcome of the appeal and its potential impact on Apple, it may be prudent for the actuator to closely monitor the progress of the case and assess any updates or rulings that may arise the future. The actuator can consider incorpo- rating this information into their decision-making process, especially if they have a significant exposure to Apple stock or related assels in the hyperportfolio.
Ultimately, the decision to buy or sell assets related to Apple or adjust the hyperportfolio should be based comprehensive evaluation of various factors, including the outcome of the appeal, market conditions, and the actuator's risk lolerance and investment strategy.
The second ask called by the Hypothesis & Proof analyst:
[Analyst]] Ask assistant: Has there been any significant market reaction to news of Apple filing an appeal in the Epic Games antitrust case?
[Assistant] Based on the search results, there is no direct evidence of any significant market reaction to the news of Apple filing appeal in the Epic Games antitrust case. However, it is important to note that market reactions can complex and influenced by various factors. To get comprehensive understanding of any market reactions, it would be beneficial to analyze the stock price of Apple (AAPI. over time and compare it to the timeline of the news and events related to the Epic Games antitrust case. Additionally, monitoring the news and expert opinious related Apple's legal battles and antitrust concerns can provide further insights into potential market reactions.
[Analyst] Based on the information available, there is no direct evidence to indicate a specific market reaction to Apple filing an appeal in the Epic Games antitrust case. However, it is important to note that market reactions can be influenced by multitude of factors and may not always be immediate easily observable. News and legal developments related to major companies like Apple can have an impact on investor sentiment and market dynamics over time.
To gain better understanding of the potential market reaction, it may be useful to monitor any updates or developments related to the court case, as well any commentary analysis from industry experts and market observers. This ongoing analysis can help inform investment decisions and portfolio management strategies.
It also important to consider that individual stock movements are influenced by various factors, including company-speciff news, broader market conditions, and investor sentiment. Therefore, it is crucial to conduct comprehensive research and analysis when making invest- ment decisions and managing a hyperportfolio.
As an analyst, it can be helpful to keep track of any new information and developments related to the Apple-Epic Games case and its potential impact on market sentiment and Apple's ' business operations. This ongoing monitoring can provide valuable insights for optimizing the hyperportfolio.
The third ask called by the Hypothesis & Proof analyst:
27

---

Published as a conference paper at ICLR 2024
[Analyst] Ask assistant: Is there any evidence of market reactions to legal proceedings or appeals in similar antitrust cases involving large technology companies?
[Assistant] Based on the search results, I found information regarding antitrust cases involving large technology companies, specifically the case against Microsoft Corporation in 1998. In this case, Microsoft was accused using significant market share in the personal computer operating systems business to prevent competition and maintain a monopoly. The case re- sulted in a federal district judge ruling that Microsoft should be broken into two technology companies, but this decision was later reversed appeal by higher court.
Unfortamately, I couldn't find specific market reactions legal proceedings appeals similar antitrust cases involving large technology companics in search resulls provided. It's possible that more specific information or analysis on market reactions in such cases may require further research or analysis.
Please note that the search results provided are limited and may not encompass all relevant information. Further research analysis may be necessary gather comprehensive evidence on market reactions legal proceedings or appeals similar antitrust. cases involving large technology companies.
[Analyst] Based on the available information, there is limited evidence regarding the market reactions to legal proceedings appeals in similar antitrust cases involving large technol- ogy companies. The search results did yield specific information analysis on market reactions to legal proceedings or appeals cases such as the Microsoft antitrust case in 1998.
However, it is important to note that market reactions to legal proceedings can be complex and influenced by various factors, including the specific details of the case, the potential impact the company's operations and profitability, investor sentiment, and broader market conditions. Market reactions may also vary depending on the outcome or ruling the legal proceedings.
To gain a comprehensive understanding of potential market reactions to legal proceedings appeals in antitrust cases involving large technology companies, it may be beneficial to conduct further research and analysis, including examining historical precedent consulting industry experts and market analysts.
the context of Apple filing an appeal in the Epic Games antitrust case, monitoring future developments in the case, as well as any updates or expert opinions, may provide insights into potential market reactions. This information can help inform the actuator's decision-making process regarding the hyperportfolio and the management of Apple-related assets.
Ultimately, investment decisions should be based including legal proceedings, market conditions, company performance, and risk tolerance. Regular monitoring and evaluation of relevant news and events can help oplimize the hyper- portfolio and respond to polential market movements.
thorough analysis of various factors,
I'm ready to give the final analysis report now.
G.2.2 PLAN & SOLVE
The only ask called by the Hypothesis & Proof analyst:
[Analyst] Ask assistant: What is the Epic antitrust case against Apple about?
[Assistant] The Epic antitrust case against Apple, officially known as Epic Games v. Apple, is a lawsuit brought by Epic Games against Apple in August 2020. The case was filed in the United States District Court for the Northern District of California. According search results from the wikisearch, Epic Games filed the lawsuit against Apple for antitrust. violations and auti-competitive behavior, along with a similar lawsuit against Google. The lawsuit alleges that Apple's 's app store practices violate antitrust laws. More information about the be found in the search results.
the
[Analyst] Thank you for the information. Based on the news article, planning to appeal the verdict the Epic antitrust case. The article mentions that Apple
that Apple is
28

---

Published as a conference paper at ICLR 2024
wants to slay the judge's order open in-app communicationss that could allow cheaper payment options. The case focuses on how big technology companies, like Apple, control access to their platforms and the restrictions they impose on app developers.
From an investment perspective, this news may have implications for Apple's business oper- ations and its relationship with app developers. It is worth monitoring how this legal battle unfolds and any potential changes in Apple's App Store policies. Any significant developments the case could impact Apple's reputation and potentially
stock performance.
this point, does not provide a clear indication to buy or Apple stock. However, it is essential to keep an eye on any updates regarding the case and how it might impact. Apple's financials and market position.
I'm ready to give the final analysis now.
G.3 ANALYSIS REPORT
G.3.1 HYPOTHESIS & PROOF
Summary
The news reveals that Apple has filed a notice of appeal in the Epic Games antitrust case. This appeal comes after high-profile trial where a federal judge ruled in favor of Apple on most counts but issued an injumction requiring Apple to loosen some restrictions
how App Store developers seek payments. Both Apple and Epic Games have filed notices of appeal, indicating their intentions specific details about the potential impact of the appeal, it highlights the ongoing legal battle between Apple and Epic Games and the implications for the App Store's rules and business practices.
challenge the court's ruling. While the news does not provide
Hypothesis
1. The appeal in the Epic Games antitrust case could lead to potential changes in Apple's App Store rules and business practices.
2. The outcome of the appeal could have implications for Apple's financial performance and its stock price.
Proof
1. The news confirms that Apple has filed notice of appeal in the antitrust case against Epic Games, indicating their intention to challenge the court's 's ruling and potential changes to the App Store's rules.
2. The trial and the subsequent appeal have drawn significant attention from lawmakers, regulators, and market participants, highlighting the potential impact on Apple's business practices.
Intuitions
1. The ongoing legal battle and uncertain outcome the appeal may create volatility and uncertainty for Apple and its stakeholders, including investors.
2. Market reactions to legal proceedings and appeals in high-profile antitrust cases involving technology companies be complex and influenced by various factors such as investor sentiment, broader market conditions, and the specific details of the case.
Advice
1. The actuator should closely monitor the progress the appeal and any updates or rulings related to the case.
29

---

Published as a conference paper at ICLR 2024
2. Considering the polential impact Apple's business practices, it is imporlant for the actuator assess the potential risks and opportunities associated with Apple's stock and related assets the hyperportfolio.
3. Stay informed about the developments in the Epic Games antitrust case and seek expert opinions and analysis to make informed decisions regarding the hyperportfolio, taking into account factors such the outcome of the appeal and market conditions.
References
WSJ: "Apple Filing Notice of Appeal in Epic Antitrust Case, Looks to Stay In-App Injunc- tion' (Author: Tim Higgins)
CNBC: 'Apple files appeal in Epic Games that could delay changes" (Oct 2021)
Bloomberg Law: "Epic Games Apple Ruling: Antitrust Litigation Takeaways' (source: Bloomberg Law)
G.3.2 PLAN & SOLVE
Summary
The news article discusses Apple's plan appeal the verdict in the Epic antitrust case. The case revolves around Apple's App Store practices
restrictions imposed on app developers. Apple is seeking to stay a judge's order to open in-app communications that could allow cheaper payment options for developers. This legal battle has drawn attention to how big technology companies control access their platforms.
Analysis
1. Legal Implications: The outcome of the Epic antitrust case will have legal implications for Apple. If Apple's appeal is successful, it could reinforce the company's 's control over its App Store and the restrictions it imposes. Ou the other hand, if the appeal is denied, Apple may be required make changes to its App Store policies, potentially impacting revenue streams.
2. App Store Business Model: Apple's App Store is a significant source revenue for the company. The article mentions that approxinalely one-fifth of Apple's 's operating income in fiscal 2019 from its cut of app revenue. Auy changes in App Store policies regulations could have financial implications for Apple.
3. Developer Relations: The outcome the case could also impact Apple's relationship with app developers. If Apple required to allow cheaper payment options, it may affect the revenue generated by in-app purchases and subscriptions. Developers may have more flexibility and potentially lower costs, which could impact their willingness continue developing for Apple's platform.
Recommendation
Based the current information available, it is difficult to determine the impact of the Epic antitrust case Apple's stock price financial performance. It would be prudent to closely monitor any developments
the case and the reactions of the market and industry. Additionally, keeping an eye on any potential changes in Apple's App Store policies and their implications for developers can provide insight into future opportumities or risks.
References
WSJ article: [insert link]
Summary of Epic antitrust case against Apple: [insert reference]
Impact App Store revenue on Apple's financials: [insert reference]
30

---

Published as a conference paper at ICLR 2024
H EXAMPLES OF ASSISTANT
We show the process of how the assistant handles the three queries made by the & P analyst in the example in Appendix G here.
The process of the first query from the H&P analyst:
[Aualyst] What is potential impact Apple filing notice of appeal in the Epic antitrust. case?
[Assistant] Search google(" impact of Apple filing notice of appeal in the Epic antitrust case")
[Function] Return for your query potential impact of Apple filing notice of appeal in the Epic antitrust case:
('top 1 result': ('url':
apple-files-appeall "title': 'Apple files appeal in Epic Games delay-changes--haaee
epic-games- case-that
that could delay ...', 'description' 'Ost 2021 In September, federal judge Yvonne Gonzalez Rogers ruled favor of Apple for nine of 10 counts in an antitrust trial brought by Epic, the maker of Fortnite.'), 'top 2 result':
(omitted here)
[Assistant] Based the search results, Apple has filed a notice of appeal in the Epic Games antitrust The filing of this appeal could potentially delay any changes to the App Store September, federal judge Yvonne
rules that were ordered by the court. According to CNBC, Gonzalez Rogers ruled in favor of Apple for nine out of ten counts in the antitrust trial brought by Epic Games, the maker of Fortnite (source: [CNBC] /twp///wwwww..pppttptpp//p///p wmm..com///e....ooo.mmm........m /202//21//// 2202//0000 11//08/ saple-flees-appall apppal--n-eppc---------------eee tn--pii-games-eeeeee waw-gga-ee---taat-oooooo whwtthhoooldddell---eeeeeeeeeeee chanle--hhangsshhmm))). This ruling was seen a resounding victory for Apple the case. Bloomberg Law also reports that Epic Games has filed notice of appeal, indicating their intention challenge the court's ruling (source: [Bloomberg Law] (https:/ www.ssooooobblgaww.coo www.oooo/oonnniee epic-games-v- htwp:llnnwnnnittrsslliiiiiio-iio
(https:/
While there is specific information available about the potential impact of the appeal, it is clear that both Apple and Epic Games are actively involved the legal proceedings and are seeking resolution
their favor. Further monitoring of this case and any subsequent court. decisions or actions would provide more insights into the potential impact of Apple filing a notice of appeal in the Epic antitrust case.
The process of the second query from the analyst:
[Analyst] Has there been any significant market reaction to the news of Apple filing an appeal in the Epic Games antitrust case?
[Assistant] Search_ Bearch_google(" Apppe filing appeal in Epic Games antitrust case news")
[Function] Return for your query Apple filing appeal in Epic Games antitrust case news:
('top 1 result': ('url': hhttps://news: wtesss.bloooe bloombergg cammbrrglass www.gglaw.... law. com/ antitrust/ epic-games-v- apple-ruling inn- antitrust- litigatiin-takewwayy
'title': 'Epic Games v. Apple Rul Ruling: Antitrust Litigation Takeaways', 'description': The federal judge overseeing the antitrust suit brought by Epic Games against Apple mostly sided with the iPhone maker, including ruling that Apple's Applxao.... "), 'top result';
*Oct 6, 2021
(omitted here)
[Assistant] Query Mamm__ddt((FINNAAP
[uucciio]] Return for your query FIN:AAPL:
('current': 140.3759460449,
31

---

Published as a conference paper at ICLR 2024
'history':
datetime
value
2020-10-28 04:00:00++00:00 109.225708
2020-11-03 05:00:00++00000 108.479187
(omitted 46 lines here)
2021-09-30 04:00:00++00:00 139.881683
2021-10-06 04:00:00++00000 140.375946,
'iformation': ... (omitted here)}
[Assistant] Based on the search results, there no direct evidence any significant market reaction the news of Apple filing an appeal the Epic Games antitrust case. However, it is important to note that market reactions can be complex and infiuenced by various factors. To get comprehensive understanding of any market reactions, it would be beneficial to analyze the stock price of Apple (AAPL) events related to the Epic Games antitrust case. Additionally, monitoring the news and expert opinions related to Apple's legal battles and antitrust concerns can provide further insights into potential market reactions.
time and compare it the timeline of the news and
The process of the third query from the analyst:
[Amalyst] Is there any evidence of market reactions to legal proceedings appeals in similar antitrust cases involving large technology companies?
[Assistant] Query (""mmarket reactions to antitrust cases involving large technology compa- nies")
[Function] Return for your query market reactions to antitrust cases involving large technology companies:
{'ids' [['1004962', '377417', 18874968' 1154887' 533772' 1
'distances'; [[O. [10.234721314047221,, 0.2480563521385193,, 0.24832972884178162, 0.23399601459533174,, 0.2581632433318883], 'documents': [[" Microsoft wasstimllated largely by complaints from AOL and Netscape, Microsoft's maincompetitors in Web browser technology. An4 The relationship between concentration and prices debatable. A core tenet antitrust economics is that anticompetitiv itive market bshavior follows from in-creased ratcon in an industry. Judge Richard Fosner complained, how-ever, that there was not enough "empirical riall guidance from industrialorgaaizatioo economists," " .... (omitted here)
[Assistant] Query (FNM
[Function] Return your query FIN:MSFT:
('current': 293.0999535516,
'history':
datetime
value
2020-10-28 04:00:00+00:00 201.221588
2020-11-03 05:00:00+00000 201.941611
(omitted lines here)
2021-09-30 04:00:00+000000 281.920013
2021-10-06 01:00:00+ 00:00 293.109988
'information': (omitted here)}
32

---

Published as a conference paper at ICLR 2024
[Assistant] Based the search results, I lound information regarding antitrust cases involving large technology companies, specifically the case against Microsoft Corporation in 1998. this case, Microsoft was accused using its significant market share in the personal computer operating systems business to prevent competition and maintain monopoly. The case re- sulted in a federal district judge ruling that Microsoft should be broken into two technology companies, but this decision was later reversed appeal by higher court.
Unfortunately, I couldn't find specific market reactions legal proceedings appeals similar antitrust cases involving large technology companies in the search results provided. It's possible that more specific information or analysis on market reactions in such cases may require further research or analysis.
Please note that the search results provided limited and may encompass all relevant information. Further research analysis may be necessary to gather comprehensive evidence on market reactions to legal proceedings or appeals in similar antitrust cases involving large technology companies.
I EXAMPLE OF ACTUATOR
1.1 INPUT
This is the analysis report of the latest news, article, etc. from the analyst:
Context:
The given news article highlights the impact of the Apple App Store on Apple Inc's business and profitability. emphasizes the central role of the App Store in driving Apple's profits, particularly in the gaming sector. The article discusses the App Store's dominance in the gaming economy, revenue generated from games, potential threats to Apple's position, and the increasing scrutiny from lawmakers and regulators.
Hypothesis:
The news about the App Store's impact on Apple's business will have a positive effect on the stock price of Apple Inc. (AAPL).
Evidence:
1. The growth of the App Store: The news mentions that the App Store has experienced significant growth, reaching million apps by 2018 (Apple Insider). This growth indicates the increasing popularity of apps and suggests a positive impact on Apple's business, which could polentially affect the stock price of AAPL.
2. Significance of the App Store: The article emphasizes the dominant role of the App Store in the gaming economy, where Apple takes 30% cut of sales from thousands of games. It states that Apple raked in more profits from games than gaming giants Microsoft, Nintendo, and Sony combined (WSJ). This reinforces the importance of the App Store in Apple's business model and implies patential positive impact on AAPL's stock price.
Proof and Intuition:
Based the evidence provided, including the growth of the App Store and its significance Apple's business, reasonable infer that the news about the App Store's impact will likely have positive effect on the stock price AAPL. The strong revenue generated from games and Apple's dominance the gaming economy further support this hypothesis.
However, it is important to consider that stock prices are influenced by various factors, and the relationship between news and stock prices can be complex. Other factors such as overall market conditions, financial performance, product launches, and investor sentiment. should also considered a comprehensive analysis.
Summary:
The news article highlights the App Store's crucial role in driving Apple's profits, particularly in the gaming sector. It emphasizes Apple's dominance, revenue generated from games, and the potential threats and challenges facing the company. The growth and significance of the
33

---

Published as a conference paper at ICLR 2024
App Store suggest positive impact Apple's business and potentially the stock price of AAPL.
Advice:
Based on the analysis, would be prudent for the actuator to consider the posilive impact of the App Store on Apple's business and its potential influence on the stock price of AAPL However, it is recommended to conduct further analysis, consider other factors that influence stock prices, and monitor market conditions for a holistic understanding of AAPL's perfor- mance.
Current status of the account:
{' l'account'; ('account name': 'default', 'cash'; 989900.0 0, "cash ratio'; 0.99, 'assets value'; 10000.0, 'assets ratic'; 0.01, 'total value' 999900.0, 'portfolios': ('default': ('total': 10000.0, 'FIN:ECA' ('symbol': "FIN:HCA', 'name': "HCA Healthcare Inc. Common Stock', 'value': 10000.0, 'price': 251. 751.212251660,, 'unrealize': 0.0, 'base'; 10000.0, 'size' 19. 19.863896, 'overnight_totall (omitted here)
0.0, 'ratio'; 0.01), rreeent
transactions':
Now, do your judgments, make decisions, and do function callings. Remember to include the reasoning steps in your judgments and decision-making process.
1.2 ACTIONS
[Actnator]
Query_icode("M icodee"Apple stock")
[Function] Here the top 3 candidates for the query Apple stock":
Code: FIN:AAPL (distance 0.22227633511611997)
This item close price time series a financial instrument including stocks, ETFs, index funds, REITs, futures, currencies, indices, or cryptocurrencies, information about this financial instrument is listed below:
Name: Apple Inc. Common Stock
(omitted here)
[Actuator] Based on the analysis report and the current status of the account, is recom- mended to hold the current asset, FIN:AAPL (Apple Inc. Common Stock), as the news article suggests positive impact on Apple's business and potentially on the stock price of AAPL. The analysis highlights the growth of the App Store, its significance in Apple's business, and the strong revenue generated from games.
Considering the current cash ratio of 0.99, would be wise allocate some additional cash to increase the position in FIN:AAPL. However, we need to ensure that the asset value represented by FIN:AAPL does not exceed the risk level we are comfortable with. As per the empirical principles mentioned earlier, having the asset value between 5% to 25% of the total portfolio is considered moderate risk.
You may allocate a portion the remaining cash buy more of FIN:AAPL, but mindful of the commission cost and the risk associated with concentrating too much in single asset. It is imporlant to maintain a diversified portfolio to mitigate risk.
[Actuator] Trade("BUY FIN:AAPL 49900")
[Fuccioo]] Succeed. All instructions exccuted. After executing the instruction, the current status of the account (omitted here)
[Actnator] Given the current status of the account and the recent purchase of additional shares of APT: it is recommended to continue monitoring market conditions and news related to Apple Inc. and the App Store for potential changes that may affect the stock price of AAPL.
[Actuator] ierdee_ccoee"Gaaee search trends for 'iPhone"')
34

---

Published as a conference paper at ICLR 2024
[Function] Here the top 3 candidates for the query 'Google search trends for 'iPhone":
1. Code: WEB:SWTUI (distance 0935540001714530030
This item Google trend time series that reveals the popularity of keyword internet search, information about this keyword is listed below:
Keyword: Swifrui
process and provides side-by-side preview. Description: Toolset for building user interfaces for Apple platforms. It simplifies the coding
Category:
Software, Technology, Design
Related topics: Server Side Tracking, User Interface Design, User Engagement, User Inter- views
(omitted here)
[Actuator] Based on the information provided, the top candidates for the query 'Google search trends for 'iPhone"
not directly related to the Apple Inc.'s iPhone product. The search trends are focused on other keywords such as 'SwiftU1,,' "Mobile Developer, and "Mobile Revemue." These keywords may not provide direct insights into the popularity or demand for the iPhone.
Considering the current status of the account and the information available, it is recommended wail for relevant information opportunities before making any further decisions regarding asset purchases or sales
35


Reviews:
[{"review_id": "K41Lu5G8Ad", "reviewer": "Reviewer_apFV", "summary": "The paper introduces SocioDojo, a comprehensive and open-ended learning environment designed for the development of autonomous agents capable of conducting human-like analysis and decision-making in areas such as economics, finance, politics, and culture. Such quality of analysis is evaluated by a proposed “hyperportfolio” framework, which generalizes from financial time series to all different time series. The paper also introduces a novel architectural agent approach, the Analyst-Assistant-Actuator, specifically for the “hyperportfolio” management task. The result is evaluated by the “return” of the “hyperportfolio” in a given period of time and it is shown to outperform other approaches like Self-Ask and Plan & Solve.", "questions": "1. Instead of expected return, modern portfolio theory typically tries to maximize risk-adjusted return. Is there any specific reason that the authors do not consider the standard deviation?\n2. In Table 1, it is shown that there would be 9 research papers per day. Is this an average statistic or subscribed information that will constantly have 9 papers?\n3. How does the “return” of “hyperportfolio” change over time? How is the final “return” distributed across different time periods?\n4. For some of the time series, e.g. GDP as an economic time series, we would have existing forecasting beforehand. They might be part of the report, and the model can directly use these numbers, which are typically fairly accurate. In this case, how do you confirm that the agent utilizes the comprehensive information from different sources to make the “investment” instead of directly taking those numbers?\n5. Financial time series is typically the most noisy one compared with other time series, which should also be the least predictable. However, it is shown that the financial time series achieves the highest return. How do authors interpret such results?\n6. Typos: equation 3.1.2, Table 3.2.1 and Figure 3.2.1 are not pointing to the correct place and are not indexed correctly. Table 1 in the Knowledge Base part should be Table 2.", "limitations": "", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. SocioDojo can take in a lot of real-world information sources and knowledge base\n2. Propose AAA agent architecture, with an analyst, an assistant, and an actuator, and it seems to work well in the proposed “hyperportfolio” management task.\n3. The proposed “hyperportfolio” is novel and interesting, which takes in a large number of different time series and can be used to evaluate the agent’s overall understanding of various aspects of real world", "weaknesses": "1. The definition of POMDP seems to disconnect from the rest part of the manuscript. Those notations are not used in other parts at all. \n2. The main evaluation results are only based on a final return in the defined period, which misses other important aspects for the results to be valid. See questions below\n3. The proposed “return” of “hyperportfolio”, as some kind of evaluation metric, is very hard to interpret. While it seems that it is related to how accurately the agent is able to predict the future of various time series, it’s hard to make sense of the numerical value, especially when various time series are fused together.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WrTzR3eoGC", "reviewer": "Reviewer_jccf", "summary": "This paper introduced SocioDojo, a framework for developing ready-to-deploy autonomous agents capable of performing human-like analysis and decision-making on societal topics, e.g., finance. The authors demonstrated the use of SocioDojo by a task called \"hyperportfolio\", where agents read news and time series data and make decision on buying/selling assets. The results show that the proposed method achieves improvements of 32.4% and 30.4% compared to the state-of-the-art method in the experimental settings of \"standard\" and \"tracking\".", "questions": "* When multiple news articles sent out on the same day, did the order of news matter? i.e., to which extend the agent is permutation invariant?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "* The paper was well-written with sufficient details on data sources, work process. Ablation studies explored the factors that impact performance.\n\n* The task of hyperportfolio is carefully designed, for example, commission fee is considered.\n\n* The proposed Analyst-Assistant-Actuator (AAA) agent architecture outperformed several recent baselines such as Self-Ask and AutoGPT.", "weaknesses": "* Since GPT-3.5-Turbo was used as foundation models with a non-zero (0.2) temperature, the results should not be fully deterministic, while the paper may miss some studies on randomness.\n\n* The setting of \"Forbid day trading\", that an asset cannot be sold within 5 days of purchase to avoid profiting from short-term patterns, might be overly strict.\n\n* For the experimental setting of \"tracking\", it remains unclear how the portfolio performs against an actual index tracker.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kDzouhulX7", "reviewer": "Reviewer_qsUb", "summary": "This work tries to propose an open-ended lifelong learning environment designed for developing autonomous agents that can perform human-like and up-to-date analysis and decision-making on societal topics. To do so, the environment SocioDojo is structured to emulate the historical development of society using real-world texts and time series data. \n\nSpecifically, it 1) integrates a continuous stream of real-world texts and messages from various information sources, including news, social media, reports, and more. The information in the form of time-stamped messages, ensures that the agents are exposed to up-to-date societal developments, forcing them to analyze and respond to fresh data.\n2) incorporates a vast array of time series data spanning various societal aspects like finance, economics, politics, and social trends.\n3) incorporates multiple knowledge bases and tools, including books, journals,  encyclopedias, interfaces to search engines and knowledge graphs, etc.\n\nWith the environment, the task of the agent is called \"Hyperportfolio Task\". They are given an initial \"cash\" value and are tasked with making \"investments\" in different \"assets\" that correspond to various time series. The goal is to maximize their total assets over a specified period, just like investment and asset management in the real world.\n\nIn this work, the author also proposes the Analyst-Assistant-Actuator agent architecture to tackle the hyperportfolio task, and the Hypothesis and proof prompting technique for generating high-quality analysis, which achieves improvements of 32.4% and 30.4% in two experiment settings compared to the state-of-the-art methods. The ablation study results show 1)the importance of domain-specific analysis techniques and high-quality information sources. 2)The analyst is critical for the hyperportfolio task.", "questions": "Are there any data leakage between the knowledge base and the streaming messages?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "Overall, this paper could be a significant contribution to the research question of \"How can we get an environment designed for developing autonomous agents that can perform human-like and up-to-date analysis and decision-making on societal topics.\" \nThe writing is clear and the paper is easy to follow.\nThe proposed approach to using diverse and real-world information sources to simulate the societal environment can truly grapple with the complexities and nuances of real-world information, which facilitates the exploration of general intelligence. \n\nThe introduction of the hyperportfolio task is novel to me, I do not think such kind of automatic metric is the best way to evaluate agents's foresight and strategic thinking ability but it is good enough. \n\nThe setting of the environment is also meaningful. Such as the prohibition of \"day trading\"  ensures that agents don't exploit short-term fluctuations but instead focus on understanding and predicting more meaningful, long-term societal trends. \n\nOverall, I do believe the complexity of the hyperportfolio task could set a high bar for LLM-based agents, challenging the community to rise to the occasion.", "weaknesses": "While the SocioDojo environment is comprehensive and emulates the running of the world, its complexity might make it challenging for researchers to quickly adapt.\n\nI think it would be beneficial to introduce some actual real-world classic scenarios or investment cases. These could serve as short-term goals or benchmarks, allowing researchers to run and evaluate their own model in a phased manner. It could also be useful for evaluating the information coverage level of the system.\n\nThe lower bound of the system is not clear. The complexity of SocioDojo might inadvertently obscure the foundational or simpler strategies that also be effective. For instance, SOTA time-series forecasting methods could be employed as a foundational strategy for investment within the SocioDojo environment. It could serve as a baseline or a lower bound against which more complex strategies can be compared.\n\nI also hope the author put more discussion on how the authors have addressed potential biases, screened sources, and ensured the diversity of data, it would bolster the paper's credibility and address potential concerns.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (spotlight)