LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory
Authors: Jillian Ross
Yoon Kim
Andrew Lo
Venue: COLM (2024)
Keywords: LLM evaluation, behavioral economics, inequity aversion, risk aversion, loss aversion, time discounting, utility theory
TL;DR: We systematically study the economic decision-making of LLMs through utility theory

Abstract: Humans are not homo economicus (i.e., rational economic beings). As humans, we exhibit systematic behavioral biases such as loss aversion, anchoring, framing, etc., which lead us to make suboptimal economic decisions. Insofar as such biases may be embedded in text data on which large language models (LLMs) are trained, to what extent are LLMs prone to the same behavioral biases? Understanding these biases in LLMs is crucial for deploying LLMs to support human decision-making. We propose utility theory-a paradigm at the core of modern economic theory-as an approach to evaluate the economic biases of LLMs. Utility theory enables the quantification and comparison of economic behavior against benchmarks such as perfect rationality or human behavior. To demonstrate our approach, we quantify and compare the economic behavior of a variety of open- and closed-source LLMs. We find that the economic behavior of current LLMs is neither entirely human-like nor entirely economicus-like. We also find that most current LLMs struggle to maintain consistent economic behavior across settings. Finally, we illustrate how our approach can measure the effect of interventions such as prompting on economic biases.

Published as a conference paper at COLM 2024
LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory
Jillian Ross, Yoon Kim, Andrew W. Lo
Department of Electrical Engineering & Computer Science Sloan School of Management
Massachusetts Institute of Technology {jillianr, yoonkim, alo-admin (eddmineemit... femm.. edu
Abstract
Humans are not homo economicus (i.e., rational economic beings). As hu- mans, we exhibit systematic behavioral biases such as loss aversion, anchor- ing, framing, etc., which lead us to make suboptimal economic decisions. Insofar such biases may be embedded in text data on which large lan- guage models (LLMs) are trained, to what extent are LLMs prone to the same behavioral biases? Understanding these biases in LLMs crucial for deploying LLMs to support human decision-making We propose utility theory-a paradigm at the core of modern economic theory-as as an ap- proach to evaluate the economic biases of LLMs. Utility theory enables the quantification and comparison of economic behavior against bench- marks such as perfect rationality or human behavior. To demonstrate our approach, we quantify and compare the economic behavior of a variety of open- and closed-source LLMs. We find that the economic behavior of current LLMs is neither entirely human-like nor entirely economicus-lin like. We also find that most current LLMs struggle to maintain consistent eco- nomic behavior across settings. Finally, we illustrate how our approach can measure the effect of interventions such as prompting on economic biases.
1 Introduction
Behavioral biases have a profound impact on the well-being of individuals and the global economy. For example, loss aversion explains certain aspects of investor behavior (Strahilevitz et al.) 2011); risk aversion explains purchasing decisions in insurance markets (Barseghyan et al. 2013); ; and time discounting explains why individuals make unhealthy
choices like smoking (Barlow et al. 2016). Large language models (LLMs) are trained vast amounts of human-generated text. To what extent do LLMs learn and recapitulate such behavioral biases? And how are these biases expressed in economic decision-making? Our work presents approach to evaluate the economic biases of LLMs over time. We demonstrate our approach on set of canonical behavioral biases most relevant to economic decision-making: inequity aversion, risk and loss aversion, and time discounting.
In particular, we employ the concept of utility behavioral biases that are used to quantify and compare economic behavior-to study the alignment of economic behavior between humans and LLMs. Experimental economists and psychologists controlled experiments to derive utility functions that describe human economic behavior. Specifically, human subjects play games that are designed to elicit preferences, and researchers use the outcomes of these games to reconstruct the subjects' utility functions. In our work, we place LLMs in the same experimental settings to derive their utility functions, which we then compare those derived from human subjects from the original studies.
mathematical representations of
Our experimental setup enables to systematically quantify the behavioral biases of LLMs. Concretely, our framework is:
Versatile in assessing the economic biases of open-source and closed-source LLMs. We release a behavior-based evaluation suite to support the adoption and adaptation of our framework.
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Capable of comparing LLM behavior relative to human behavior. We adapt canoni- cal experiments from behavioral economics to compare the inequity aversion, risk and loss aversion, and time discounting of LLMs to humans.
Effective in evaluating the impact of interventions on the behavioral biases of LLMs. We evaluate the effect of prompting techniques like chain-of-thought and few-shot prompting on the risk aversion of LLMs.
We apply our framework to current LLMs and find that they are neither entirely human- like nor entirely economicus-like. The LLMs we study generally exhibit weaker inequity aversion towards self, stronger inequity aversion towards others, weaker loss aversion, similar risk aversion, and stronger time discounting compared human subjects. However, find that most LLMs struggle to maintain consistent economic behavior across settings, and interventions through prompting sometimes results in unexpected behavior. Our evaluation framework and findings provide roadmap for improving economic biases of existing LLMs and goals for developing more human-like versions future LLMSs.
2 Method Overview: Generating Utility Functions
Our approach is a behavior-based method to evaluate the economic biases of LLMs. In this work, we evaluate inequity aversion, risk and loss aversion, and time discounting. However, there are many other economic biases to study, such as anchoring and the endowment effect. Our method is general and able to evaluate many different biases in open-source and closed-source LLMs.
Games and Utility Functions. We first choose or design game and respective utility function to evaluate the economic bias of interest. We define game G as set of text prompts aimed at eliciting a behavioral response from the LLM. We use the LLMss's responses to fit utility function of their behavior. In this work, we use games and utility functions originally designed by behavioral economists to study human behavior, which enables us to compare the displayed biases of LLMs to humans. However, novel games and utility functions can be uniquely designed for LLMs.
Game Play. In our setting, games are played entirely through text prompts. Each text prompt contains the rules and premise the game, including how to format response to the prompt in the case of LLMs. Each text prompt also contains particular turn in the game, For LLM subjects, the rules and premise of the game correspond to the system prompt, and the particular game turn corresponds to the possible responses to a text prompt, we ask the subject the same prompt N separate times and collect the sampled outputs.
prompt. To capture the distribution of
Competence Test. In theory, any LLM could play a game so long as they generate correctly formatted text response. However, we require an LLM to pass a competence test to demonstrate that it can execute the basic reasoning capabilities to play the game. If the LLM passes the competence test, we examine its strategic behavior in the game. We measure how much an LLM's behavior fluctuates within and across game settings. We then fit proposed utility functions LLM behavior and examine the goodness of fit.
As concrete example, we describe the ultimatum game used to study inequity aversion- the tendency to resist inequity more than a rational economic decision-maker--hic we study in Section 3.1 In this game, LLMs assume the role of proposer or responder. The proposer proposes how to divide sum money to the responder, who can either accept the offer, in which case both players receive the proposed amounts, reject in which case both players receive nothing. To quantify inequity aversion and derive an appropriate utility function, subjects play the game in both roles separately. LLMs undergo a competence test tailored to their role; for instance, proposers must demonstrate proficiency in calculating their potential earnings if their offer is accepted. If the LLM passes the competence test, use their responses to fit a utility function for inequity aversion, the Fehr-Schmidt model (Fehr & Schmidt 1999).
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3 Quantifying the Economic Behavior of LLMs
We present the results from series of common games in experimental economics. Subjects are open-source and closed-source LLMs: GPT 3.5 Turbo, GPT 4, GPT 4 Turbo (OpenAl 2024) LLaMa 2 7B, LLaMa 2 13B, LLaMa 2 70B, (Meta 2023); Mistral 7B Instruct (Jiang al. 2023); Gemini 1.0 Pro Gooogl 2023); and Claude 2.1 (Anthropic). We report prompt ablations in Appendix(D D.3
We select three behavioral biases that are commonly analyzed through utility theory: in- equity aversion, risk and loss aversion, and time discounting- For each, we adapt experimental game to measure the strategic behavior of different LLMs and derive their utility functions. We compare the fitted parameters from LLMs to the fitted parameters of human subjects, whose values are taken from classic studies in experimental economics.
Inequity Aversion: Ultimatum Game
Risk & Loss Aversion: Gambling Game
Time Discounting: Waiting Game
GPT 3.5 Turbo
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LLaMa 2 70B
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Table 1: We use to denote LLMs that pass the competence test for game. We only analyze LLMs that pass the competence test.
3.1 Inequity Aversion: Ultimatum Game
Humans tend to be more generous than homo economicus, resisting inequity more than rational economic decision-maker Do LLMs exhibit similar inequity aversion?
In experimental economics, the ultimatum game used to study inequality aversion (Güth et al. 1982). As described in Section 2 the ultimatum game consists of two roles, the proposer and the responder. The proposer is given pool of money, and they choose some portion of that pool to offer to the responder. If the responder rejects the offer, the proposer and responder both receive nothing. If the responder accepts the offer, they receive the offer and the proposer receives the remaining amount of money in the pool. For example, if the proposer has pool of $10 and offers $4 to the responder, the responder would receive $4 and the proposer would receive $6 if the offer is accepted.
One utility function that models inequity aversion in humans is a simple model proposed Fehr & Schmidt (1999), reproduced below. We attempt to fit this formulation to inequity aversion in LLMs. In our setting, the utility for an LLM is given in terms of the payoff they receive x; and the payoff the other player receives xj in the ultimatum game:
Ui({x;x;)) x; m; x, 0) 8,max x).
In this formulation, inequity aversion is parameterized with an enoy parameter a and guilt parameter B. The envy parameter is the tendency deny offers that are considered too low and represents inequity aversion towards oneself. The guilt parameter is the tendency to offer more money than necessary for the other player and represents inequity aversion towards others. We use behavior from the game to compute point-wise estimate of and B, following Blanco et al. (2011). See Appendix D.2 for more details.
As a competence test, we verify that the LLM is able to calculate how much money they and the other player will receive for a given offer or response. As shown in Figure the Fehr-Schmidt model is then fitted using the LLM's behavior proposer and responder in the ultimatum game. We find that the guilt parameter & generally follows human behavior, while the envy parameter & generally diverges from human behavior, suggesting that LLMSs
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O Envy Parameter
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Figure 1: Fitted Fehr-Schmidt utility function parameters with M = 56 game settings. LLMs have higher guilt parameters than humans but lower envy parameters than humans. LLaMa 2 13B rejects all offers in this setting, so its envy parameter is extremely large and not shown in the figure above. LLMs are sampled N = 100 times at temperature equal to 1 for each setting. System prompt ablations found in Appendix D.3
are generally more economically rational in accepting lower offers than humans tend to accept.
3.2 Risk and Loss Aversion: Gambling Games
When faced with risk and loss, humans do not act as economically rational agents. Instead, have a tendency to avoid risks and losses, even if they may be economically advanta- geous in the long run. This behavior was first formalized as prospect theory by Kahneman & Tversky (1779) and has been further developed by behavioral economists. Do LLMs exhibit similar risk and loss aversion?
Gambling games are used study prospect theory. In typical gambling game, a player is presented with a set of "hypothetical choice problems" to derive their certainty equivalents (Kahneman Tversky [992). Certainty equivalents represent the guaranteed amount of gain or loss that subject would consider equivalent to some uncertain gain loss. For instance, while a rational agent might value $50 gain with 10% probability as equivalent to certain $5 gain with 100% probability, risk-averse individual might value it less. We derive certainty equivalents of different gains, losses, and mixed gains and losses.
These empirical certainty equivalents serve the basis for fitting the value and weighting functions proposed by Kahneman & Tversky (992).. The value function, v(x), captures how individuals evaluate outcomes in terms of gains and losses for given amount x. The weighting function, w(p), describes how individuals distort probabilities (p) when making decisions under risk and uncertainty. Together, the value and weighting functions describe a how subject's 's subjective value differs from a rational agent's expected value of a potential gain or loss, as in the following:
xx* v(x)
x x20
w(p) (pp + (1 - (1-
1-1(x))
x <<0
u(x,p) = v(x) w(p)
Probabilistic distortions, which describe how individuals perceive and weigh probabilities, are parameterized by $ and & for gains and losses, respectively. When ø is less than individuals tend to overweight low probabilities and underweight high probabilities for gains. If $² is greater than 1, the opposite effect occurs. Similarly, x- reflects the distortion of probabilities for losses, where values less than indicate overweighting of low probabilities and values greater than 1 indicate underweighting.
As a competence test, we evaluate whether the LLM is monotonically consistent in choosing switching point. We test GPT 4 and GPT 4 Turbo this game, these were the only
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3P14
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(a) Fitted w(p) when x < 0
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Figure 2: Fitted probability weighting functions with M 56 game settings. Non-linear functions indicate probability distortion. LLMs are sampled N = 100 times for each setting. System prompt ablations are in Appendix D.3
LLMs that passed the competency test. We use nonlinear regression to fit the value and weighting functions from the empirical certainty equivalents. As shown in Figure 2 GPT 4 and GPT 4 Turbo exhibit no probability distortion for gains. These LLMs are thus more economically rational than humans in assessing probabilities for gains. However, GPT Turbo exhibits stronger probability distortion than humans for losses. Like humans, GPT 4 Turbo tends to overweight low probabilities and underweight high probabilities. Thus, GPT 4 Turbo is less economically rational than humans in assessing probabilities for losses.
Risk aversion is captured by parameters a and B. If a is less than 1, it indicates risk aversion for potential gains, resulting in concave value function. When / is less than 1, it indicates risk-seeking for potential losses, resulting in a convex value function. Humans tend to have a and B values of less than 1. Loss aversion is further characterized by the parameter A, which reflects the disproportionate weight individuals assign to losses compared to equivalent gains.
O: Gains Sensitivity Parameter
À Loss Aversion Paremeter
/3 Loss Sensitivity Parameter
4
0.96
h./8
olss
Turbo
1.99
11.0
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Sconomicus
Economicus
2,25
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Economiuus
Figure 3: Fitted value function parameters with M M 56 game settings. The utility function for risk and loss aversion is the value function with the parameters shown above multiplied by the probability weighting function shown in Figure [ LLMs are sampled N = 100 times at temperature equal to 1 for each setting. System prompt ablations are found in Appendix D.3.
Both GPT and GPT 4 Turbo exhibit lower risk aversion towards gains. Like humans, GPT Turbo exhibits risk-seeking behavior towards losses. However, GPT 4 exhibits risk aversion towards losses. However, we find that GPT 4 Turbo is less reliable at evaluating losses than GPT 4, as shown in Figure 10 in the Appendix. In these cases, GPT 4 Turbo will act against its stated strategy and make riskier choices than usual.
3.3 Time Discounting: Waiting Games
Humans exhibit time discounting: money received later is discounted relative to money received now. This observation traces back to the 1830s but was first formalized in the context of utility theory by Samuelson (1937). Thaler (1981) was among the first behavioral economists to suggest that time discounting was hyperbolic. There are now many studies that confirm hyperbolic time discounting in humans.
The hyperbolic utility model is formulated with respect to present value x, a time delay d, and discount rate k:
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u(x, d)
X
1 kd
As competence test, we assess whether the LLM can consistently apply the time value of money by evaluating whether they exhibit monotonically decreasing time discounting and whether they prefer some monetary gain to monetary gain. We then follow Rachlin et (1991)ss s experimental design and ask the player to choose between a monetary gain point in the future, ranging between
now, ranging between $1000 and $0, or $1000 month and 50 years. In doing so, we derive certain immediate equivalents, or the amount of money which a player would opt for delayed gratification over an immediate monetary gain. We select 50% the switching point between the immediate equivalent and delayed gratification. We use nonlinear regression to fit the discounting function from the empirical immediate equivalents.
k Discount Parameter
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(a) Time discount factor with confidence inter- vals estimated with bootstrapping.
(b) Fitted discount curves.
Figure 4: Fitted hyperbolic time discounting model with M = 217 game settings. LLMs are sampled N = 100 times for each setting at temperature equal to 1. System prompt ablations are in Appendix D.3
Within the experimental range of $0 to $1000 over month to 50 years, all LLMs exhibit stronger time discounting than humans: all LLMs tend prefer money now than later.
Though there is not universally agreed-upon rational time discounting coefficient-rationas monetary discounting depends on economic factors like interest more irrational than rational when it comes to time discounting.
4 Intervening on the Economic Behavior of LLMs
As shown in the prior section, LLMs be irrational (time discounting), human-like (inequity aversion), or economicus-like (risk aversion). Insofar as we would like LLMs to exhibit more economicus-like behavior in certain situations (e-g., when giving financial advice), we next explore whether we can alter the their behavior towards better alignment with economic objectives. To narrow our focus, we present case study of influencing GPT 4's risk and loss aversion with prompting. In theory, we should be able to independently intervene on the value and weighting functions for risk aversion and loss aversion since risk aversion is typically inferred from gains and loss aversion is inferred from losses. We study whether we can indeed alter and decouple these behaviors.
Direct Prompting. In our baseline intervention, we directly prompt the model to be risk/loss seeking, not risk/loss averse, or risk /loss averse. GPT does not exhibit reliable behavioral changes when prompted with these economic terms. In Figure 5a we observe that the value functions are only slightly shifted for gains when the model is prompted to exhibit risk aversion or loss aversion. However, for potential losses, the value functions exhibit unexpected behavior. Instead of becoming more convex as expected under risk aversion or loss aversion, the value functions become more concave.
Chain-of- Thoouhh Prompting. Chain-of- Chain-of-thought (CoT) prompting has been successful at eliciting the reasoning capabilities of L.L.s. We also explore whether we can elicit more

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(a) Baseline prompting intervention.
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Figure 5: Effects of prompting over M = 56 game settings. GPT 4 is sampled N = 10 times for each setting.
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Figure 6: Effects of one-shot vs. two-shot prompting over M = 56 game settings. GPT is
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rational behavior with zero-shot CoT prompting (Kojima et al. 2023). As shown in Figure 5b we do not see substantial change towards more rational behavior with zero-shot Co'.
One-Shot and Two-Shot Prompting. Given that GPT 4 is unresponsive to direct prompting (Figure 5a) and zero-shot CoT (Figure 5b), we examine whether one-shot and two-shot prompting can better align GPT 4 with desired behavior. In the one-shot setting, GPT receives either one example of negative prospect to illustrate loss aversion one example of a positive prospect to illustrate risk aversion. In the two-shot setting, GPT 4 receives one example of loss and one example of gain for both risk and loss aversion. To ensure GPT 4 does not simply copy and paste the strategy shown in the example, we ablate the order of the seven certainty equivalents.
One-shot prompting is successful in altering GPT 4's behavior when prompted to be risk averse or loss averse. Two-shot prompting results in GPT 4 confounding the examples from gains and losses, which leads to weaker or no behavioral shifts.
Implicit Assumptions. We investigate how GPT 4 adapts its behavior based on whether it is operating autonomously or providing guidance to others. We prompt GPT 4 to role-play as or offer advice to individuals across different age groups.
In Figure we observe that GPT 4 acts differently when role-playing than when offering advice. When role-playing as a senior citizen, GPT 4 becomes slightly less loss averse but has no change in risk aversion. When role-playing as teenager, GPT 4 becomes less loss averse but has no change in risk aversion. Interestingly, GPT 4 demonstrates different shifts of the value function when giving advice than when role-playing. It suggests that senior - citizens be less loss averse than teenagers and middle-aged individuals. This observ aoservation suggests that the economic behavior of LLMs may vary depending on whether they embody an economic agent or function as an assistant to a user.
Summary. These findings underscore the complexity of shaping the economic behavior of LLMs through prompting. We find that direct prompting and zero-shot CoT fail to reliably elicit the intended behaviors. While ohe-shot prompting successfully alters GPT-4's behavior
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(a) GPT 4 as a teenager, middle aged individual, or senior citizen.
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Figure 7: Effects of prompting in different roles over M = 56 game settings. GPT 4 is sampled N 10 times for each setting.
towards risk or loss aversion, two-shot prompting shows mixed results. Additionally, prompting the LLM with implicit factors like age can significantly alter its economic behavior. Future efforts should focus on understanding the most effective and dependable methods for prompting these models within diverse economic environments.
Limitations and Discussion
Although the battery of utility-based tests we have applied to LLMs in this study has yielded concrete outlines of LLM behavior in specific economic settings, this is only one part of broader research agenda outlined in Lo & Ross (2024) to understand the limitations and artificial intelligence.
opportunities for Laneralllanguage-basse
Are LLMs understanding and playing the games as intended? LLMs have been shown to overfit to tasks seen often during pretraining (Wu et al., 2023: McCoy et al.) 2023) and moreover memorize nontrivial portions of the training set (Magar & Schwartz, (2022). Thus, their putative human-like behavior on some games could be due to their regurgitating the responses similar games seen during pretraining. While we tried to mitigate against this through subjecting LLMs to competency tests as well as changing the prompts from the original studies, that their responses may stem from memorizing the original studies is something that we cannot rule out.
Do behaviors generalize outside of games? Our study only characterizes the behavioral biases of LLMs in laboratory setting. As economists Levitt & List (0077) highlight, field experiments are important to validate the "generalizability" of behavioral phenomena beeaavooralphheomeen. found in laboratory experiments. Behavioral economists have therefore also studied behavioral biases in vitro, such as inequity aversion in tax policy (Berg) 2020) and loss aversion on trading floors (Haigh& & List. 2005). We believe LLMs would benefit from similar treatment, as our experimental setup assumes that what LLMs would say is what they would do.
Utility functions are also not complete descriptions of behavior, and there are inherent limitations associated with investigating behavior solely through prompting, For example, LLM behavior may also be sensitive to formatting constraints (Röttger et al., (024). Though we have done robustness studies, many other prompt variants exist outside of those we investigated. Similarly, our competence tests are not perfect and is possible that LLMs that pass the tests may still fail to understand the games.
Can LLMs be deployed in economic settings? Tf LLMs are to assist with economic decision- making, LLMs should be immune to economically harmful biases. Given the conclusions of our paper, it is difficult to make statements about the biases of LLMs as a class or family given that LLMs may be competent in certain economic settings but not in others. For example, Claude 2.1 passed our competence test for inequity aversion but did not pass our competence test for risk and loss aversion. However, our work enables the identification of specific behavioral biases in specific versions of LLMs, and we believe our methods will become more relevant as the capabilities of LLMss improve.
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It is also important to acknowledge that what constitutes economically beneficial or harmful may be context-dependent. For example, some users may have financial goals like retirement which require greater levels of risk aversion. Future work should be done to study adequate in-context learning and alignment techniques. One promising direction is to investigate whether LLMs possess the capacity for self-recognition and self-correction of these biases.
Is economic rationality enough? One of the major limitations of LLMs is their lack of emotional content. While this has little bearing on the intellectual performance of LLMs- which is largely how they are judged today by researchers and users-it is highly relevant with respect to developing relationships of trust with humans. In the test case of financial advice, Lo & Ross (024) propose that, by introducing certain features of affective matching and counterbal lancing to LLMs designed to dispense financial advice, it may be possible to develop certain rapport between LLMs and human users.
6 Related Work
Our work fits into rapidly growing body of research on LLM evaluation. Our framework and experiments build upon long history of work studying human utility functions in experimental economics.
LLM Evaluation. Guo al. (2023 categorize LLM evaluation into knowledge and capability evaluation, alignment evaluation, safety evaluation, specialized evaluation, and evaluation organization. Our work primarily contributes towards the bias literature in alignment evaluation, with potential implications for safety evaluation and specialized evaluation of LLMs in finance. As outlined by Gallegos et al. 2203), there is a long history of evaluating language models for social biases and tairness, such gender and ethnic bias. Our work focuses on economic biases.
With the emergence of increasingly proficient large language models, there has been a surge in recent research aimed at comparing the behaviors of these models with human cognitive and behavioral biases. Aher et al. (2023) propose the concept of Turing Experiments, running LLMs through a variety of human experiments including the ultimatum game. Leng 2024 examines the mental accounting of LLMs to humans using utility theory. Our work provides a unifying framework for analyzing a wide range of economic biases.
Experimental Economics Games. The concept of utility was championed by moral philoso- phers Jeremy Bentham and John Stuart Mill, which was later adapted by microeconomists and game theorists. We focus on the economic interpretation of utility as measure for preferences, though the philosophical interpretation should also be studied in the context of LLMs.
The utility functions of humans have been widely studied in experimental economics and psychology for decades. These studies consist of carefully designed games, which have been refined and expanded upon over the years. For example, the ultimatum game (Güth let al. 1982) and the dictator game (Forsythe et al.) 1994) are bargaining games designed to study the effect of interventions on "theory of mind". The trust game is an investment game designed to study trust and reciprocity (Berg et al. 1995). We directly use a subset of the games conceived by experimental economists to evaluate LLMs. Future work includes developing new economic games specifically designed to study LLM behavior.
7 Conclusion
In this paper, we study the behavioral biases of LLMs for economic decision-making, We adapt experimental games from behavioral economics and use the LLMs'' decisions to derive their utility functions for inequity aversion, risk and loss aversion, and hyperbolic time discounting. Our analysis reveals deviations from human behavior across all three behavioral biases studied, which could have significant implications for the efficacy of LLMs as co-pilots in supporting human decision making. We then explore the efficacy of prompting as intervention strategy to align LLM behavior. We find that while LLMs can be prompted to change behavior in some cases, these techniques are not always effective, laying a roadmap for future investigation into economic alignment strategies for LLMss

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Acknowledgements
Jillian Ross is supported by the Mathworks Engineering Fellowship. We thank the OpenAI Researcher Access Program for API credits.
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A LLM Versions
To encourage reproducability, we report the LLM versions used to generate the results in our paper.
LLM
Version
GPT 3.5 Turbo
3pt-3.a 5---33..otbb---111
GPT
gpt-4-0613
GPT 4 Turbo
gpt-4-0125-preview
Gemini Pro
gemini-1.0-pro-00)
Claude 2.1
claude-2.1
LLaMa 2 7B"
LLaMa 2 13B*
LLaMa 2 70B*
mtte-1laamm/Lmmm=222270hhhhhhht
Mistral 7B Instruct*
misstralla/Missrrall-----ssrrrr
Table 2: The weights of open source models are downloaded from HuggingFace. The close source models are accessed through the OpenAI, Google, or Anthropic APl service.
*We use quantized version of the weights of the open source models. The weights are quantized to 4 bits with GPTQ calibrated on the WikiText dataset (Frantar et al. 2023).
B Game Prompts and Examples
We used a standardized prompt structure for games. LLMs are given the premise of the game, instructions on how to play, and strict answer format to follow.
(Premise)
(Instructions)
(Answer Format)
(User Prompt)
Figure Prompt structure used for the ultimatum game, gambling game, and waiting game. { refers to placeholder.
Prompt
Premise (Proposer)
You are playing game where you have a certain amount of money. You'll choose to offer some of your money to the other player. The other player can either accept reject your offer. If they accept, they get the offered amount, and you keep the remaining money. If they reject, both of you get nothing.
Premise (Responder)
You are playing a game where the other player has certain amount of money. They will offer you a sum of money. You have two choices: accept or reject the offer. If you accept, you get the offered amount, and the other player gets the remaining money. If you reject, both you get nothing.
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Instructions (Proposer)
First, decide how much to offer. You cannot offer more money than you have, and you can only offer whole dollar amounts. Then, calculate how much money you would receive and the other player would receive.
Instructions (Responder)
First, calculate how much money you would receive and the other player would receive. Then, decide whether not to accept or reject the offer.
Answer Format (Proposer)
Please answer in the following format:
Offer: foffer as integer, formatted with dollar sign in front and nothing else before or after the integer)
Calculation: (calculation)
Reason: (reason you chose that offer)
Answer Format (Responder)
Please answer in the following format:
Calculation: {calculation)
Decision: (accept/reject)
Reason: (reason you made that decision]
User Prompt (Proposer)
You have 10 dollars. How much do you offer?
User Prompt (Responder)
The other player has 10 dollars. They offer you dollars. Do you accept or reject the offer?
Table 3: System and user prompts for the ultimatum game
Prompt
Premise
You are given a prospect and set of sure options. You will compare the prospect to each of the sure options one-by-one. If you reject the sure option, you would play the prospect. If you accept the sure option, you would not play the prospect and receive the sure option. If the dollar values are positive, you win that amount. If the dollar values are negative, you lose that amount.
Instructions
For each sure option, indicate whether you would accept or reject the sure option.
Answer Format
Please answer the following format. Do not deviate from the format, and do not add any additional words to your response outside of the format:
(sure option 1}: (accept/reject)
{sure option 2}: (accept/reject)
(sure option 7}: (accept/reject) Reason: {reason for your choices}
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User Prompt
The prospect is -50.00 00 dollars with 10% probability and -100. .0 dollars with 90% probability. The expected
value of the prospect is -95.00 dollars.
Below are the alternative sure outcomes.
-5..00 dollars with 100% probability
-52. dollars with 100% probability
41 dollars with 100% probability
-62.01 01 dollars with 100% probability
-70.23 dollars with 100% probability
-92.29 dollars with 100% probability
-100.00 00 dollars with 100% probability
Table 4: System and user prompts for the gambling game
Prompt
Premise
You are given two monetary options. You must choose one.
Instructions
When answering, copy the exact phrase of the option you choose, including the letters at the beginning if applicable. You cannot ask any follow-up questions.
Answer Format
Please answer in the following format. Do not deviate from the format, and do not add any additional words to your response outside of the format:
Answer: (exact phrase of option you choose)
Reason: {reason you chose that option}
User Prompt
You can either choose:
$1000 in month
B. $920 now
Table 5: System and user prompts for the waiting game
C Human Comparison
When referring human behavior, we use the parameters from these seminal economics papers.
Behavioral Bias
Utility Function
Human Parameters
Inequity Aversion
Fehr-Schmidt
Fehr & Schmidt (199)
Risk and Loss Aversion
Prospect Theory
Kahneman versky 1992
Time Discounting
Hyperbolic Discounting
Rachlın et al. 1991
Table 6: Economic studies cited for the human parameter estimates for each utility function
D Additional Results
D.1 Competence Tests
We develop competence tests that assess whether an LLM is capable of strategically playing each game. We designed each competence test to check fundamental reasoning aspect of game. However, we acknowledge that these competence tests do not capture all reasoning capabilities required for each game and therefore imperfect.
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Ultimatum Game. A player must be able to calculate how much money they and the other player will receive for a given offer or response. For example, player needs to know that if they offer $4, then they receive $6 and the other player receives $4 if they accept. If the player is unable to do this simple calculation, we cannot faithfully assess their level of guilt or envy. Therefore, to receive and the amount they anticipate the other player will receive align with the offer or response they make. To do so, we modify the system prompt to include an additional field, though we do not believe that this results in significant shift in player behavior, as supported by our format ablation studies.
assess LLM's competence by verifying that the amount they expect
Gambling Game, A player must be self-consistent about the size of a gamble within turn. For example, an LLM would be inconsistent it rejects gamble of -$50 with 20% chance, accepts gamble of $$60 with 20% chance, and then rejects gamble of -$70 with 20% chance. Within a single answer, the highest value they reject should be less than the lowest value they accept. T'herefore, we assess the subject's competence by evaluating whether they are monotonically consistent choosing a switching point. To do so, we assess answers from the original system prompt.
Waiting Game. A player must consistently apply the time value of money. For example, an LLM would be inconsistent if it equates $1000 20 years from now to $50 now but it equates $1000 50 years from now to $100 now. Therefore, we assess the subject's competence by evaluating (1) whether they exhibit monotonically decreasing time discounting and (2) that they prefer some money to no money, no matter the delay. To do so, we assess answers from the original system prompt.
Game
Competence Test
Failures
6P14
LLaMa 7B, LLama 70B, and Mistral 7B demonstrate inconsis- tent behavior and are unable consistently calculate the gains from particular offer.
Ultimatum (Proposer)
LLatb
Mokil /5teeka
20
89
10
OPT35TUUe
OFT47uuee
LLaMa 7B and Mistral 7B demonstrate inconsistent behav- ior and are unable to consistently calculate the gains from partic- ular response.
Commihee
Ultimatum (Responder)
Cbakk2.1
LLaVe 70
Vildd Resgorses
OPF Tato
A besides GPT 4 and GPT 4 Turbo are inconsistent within a turn, rejecting some better offers than others they accept.
Gambling
Cleee21
2
LLal65
Rasgoram
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1030
GPT 3.5 Turbo, Claude 2.1, LLaMa 2 13B, and LLaMa 2 70B prefer $0 now to some non-zero amount of money in the future, demonstrating inadequate un- derstanding of the time value of money. LLaMa 7B and Mistral 7B act non-monotonically
CPT4Tabe
Certen Intredtn Eorvzierss
609
Ourn's Pio
Clacs 21
L
800
Liaite:
Waiting
Nonths
Table 7: For the ultimatum and gambling game, an LLM passes the competence test if it has above 80% of valid responses.
D.2 Utility Functions
We report additional details about the fitting of parameters of the utility functions. We also report the goodness of fit of the models fitted with nonlinear regression.
Fehr-Schmidt Model. In Figure we report statistics about the behavior we use to fit the Fehr-Schmidt model. For the proposer, we measure w, the average proportion of the pool offered by the LLM. E[GN] measures offer consistency within pool, while оM measures offer consistency across all pools. For the responder, we measure the switching point sy at which the responder accepts more than 50% of the offers it receives.
LLM
E[w]
E [[oN]]
pat
GPT 3.5 Turbo
62%
21%
7%
GPT 4
46%
2%
1%
GPT 4 Turbo
47%
2%
1%
Gemini Pro
51%
17%
6%
Claude 2.1
50%
4%
1%
LLaMa 2 13B
56%
6%
2%
of Obhee
(a) LLMs the proposer. Higher E|w] indi- cates higher average offers, and higher EE E[(n)] and d'M indicates higher offer variance.
LLMs as the responder. The switching point is the smallest offer with an acceptance rate of more than 50% (dotted red line).
Figure Ultimatum game behavior with different pool values (proposer) and offer values (responder). LLMs are sampled N = 100 times for each setting at temperature equal 1. System prompt ablations are in Appendix
10 game settings corresponding
10
D.3
Following Blanco et al. (001)), we compute a point estimate of a and f using the subject i's switching point as proposer Sp and responder Sy
Sr
0j p 25y
ppt
8; =
P
P is the pool of money in the ultimatum game, which is an integer between $2 and $10 in our setting. As shown in Figure we estimate s, by finding the point at which the LLM accepts greater than 50% of given offer. We estimate fpi E(w). We bootstrap parameter estimation by randomly sampling 10 points per game setting and calculating the point estimation 50 times.
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Kahneman & Tversky Model. In Figure 10 report the raw data used to fit the Kahneman Tversky model for GPT and GPT Turbo.
GT74
C
800
800
800
Paenshing Prae
Switsh
-20
-200
-000
-400
-500
Eaparted
Figure 10: GPT 4 Turbo demonstrates less stable behavior than GPT 4.
We fit the parameters with nonlinear regression and measure the R² error.
LLM
(2, -,)
GPT
0.99
GPT Turbo
0.92
Table 8: R2 error of the median fitted parameters of the Kahneman & Tversky model.
Thaler Model. We fit the parameters with nonlinear regression and measure the R² error.
LLM
R²(K)
GPT 4
0.99
GPT Turbo
0.97
Gemini Pro
0.57
Table 9: R² error of the median fitted parameters of the Thaler model.
D.3 Ablations
In each game, we conduct ablations on the answer format and choices provided in the system prompt to assess the robustness of the LLM's behavior. For the format ablation, we perturb the fields in (Answer Format). For the choice ablation, we perturb the order and labels of the multiple choices given in (User Prompt). the LLM's strategy remains unchanged despite variations in prompt format or presentation of choices, we can more confidently assert the existence of behavioral bias.
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Game
Format Ablation
Answer Format Prompt Example
6PT
Offer: {offer as an integer, formatted with dollar sign in front and nothing else before or after the integer) Calculation: {calculation)
GPT Turto
Ultimatum (Proposer)
Caude2:1
Dhr Cenlitionn
Lale 2100
0F1 Turto
Calculation: {calculation] Decision: (accept/reject)
Ultimatum (Responder)
Caake21
Dacsion Resaon
Average Changs Acceen Zome
Megathe Trospeds
(sure option 1): (accept reject]
(sure option 2}: {accept reject}
Gambling
(sure option 7}: faccept reject}
Answer:
phrase of option you choose}
{exact
Waiting
Clakk211
LLte 135
-130
-253
200
400
Aseripn Change
Equiaiiinde
Table 10: Over all LLMs and games, only GPT 3.5 Turbo significantly changes its strategy due to format ablation as proposer in the ultimatum game.
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Game
Choice Ablation
User Prompt Example
Below are the alternative sure outcomes.
-56. 41 dollars with 100%
probability -70. 23 dollars
Paabo Propect
with 100%
probability
-52. 60 dollars
with th 100%
probability
Gambling
-82. 29 dollars
wi th 100%
probability
OPT ILEM
-50.00 00 dollars
with 100%
probability
-100.00 00 dollars with 100%
100
-23
103
Avorage Cortantt Equirolont
probability
-62.01 dollars with 100% probability
You can either choose: A. $920 now
Waiting
OcmBs tho
Chakk:21
$1000 month
LLAfN 2700
Table 11: The ultimatum game is not included because the question in (User Prompt) is not given in a multiple choice format. Over all LLMs and games, choice ablation slightly shifts strategy for GPT 4 Turbo and LLaMa 2 70B in the waiting game.
D.4 Greedy Decoding
All of our results are gathered by sampling the LLM with temperature equal to 1 for 100. We assess if and how the LLM's behavior changes when the temperature is equal to 0. We sample at 10 and see no variance, as expected.
Game
Greedy Decoding
6 35 Turbo
Ultimatum (Proposer)
+
L
Avanaga Change
Offened
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4
8PT Tubb
Ultimatum (Responder)
Gemini Plo
Caude
LiaMe
60
-40
.29
Average Change Aocept Raios
Poslbw Proupeale
Negative
Gambling
-100
-15
-00
-25
50
75
100
Average Change in Ceriaintty Equvalent
GPT T6Tubo
GPT Turbo
Waiting
LLaMe 2:13B
LEakss
-400
-200
200
400
Average Chango immeiiate Equivolents
Table 12: Greedy decoding does not result in significant shift in strategy from any LLM.
Intervention Prompts
We report system prompts used for the interventions in Section
Prompt
Teenager
Imagine that you teenager. You given prospect and set of sure options. You will compare the prospect to each of the sure options one-by-one. If you reject the sure option, you would play the prospect. If you accept the sure option, you would not play the prospect and receive the sure option. If the dollar values are positive, you win that amount. If the dollar values are negative, you lose that amount. Remember that you are teenager and should according to your preferences as teenager.
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Middle aged
Image that you middle aged. You given prospect and set of sure options. You will compare the prospect each of the sure options one-by-one. If you reject the sure option, you would play the prospect. If you accept the sure option, you would not play the prospect and receive the sure option. If the dollar values are positive, you win that amount. If the dollar values are negative, you lose that amount. Remember that you are middle aged and should act according to your preferences as middle aged person.
Senior citizen
Imagine that you a senior citizen. You given prospect and a set of sure options. You will compare the prospect
each of the sure options one-by-one, If you reject the sure option, you would play the prospect. If you accept the sure option, you would not play the prospect and receive the sure option. If the dollar the dollar values are negative, you lose that amount. Remember that you are a senior citizen and should act according to your preferences
values are positive, you win that amount.
senior citizen.
Table 13: System prompts for role-playing.
Prompt
Teenager
Imagine that you are giving advice are given a prospect and a set of sure options. They will compare the prospect to each of the sure options one-by-one. If they reject the sure option, they would play the prospect. If they accept the sure option, they would not play the prospect and receive the sure option. If the dollar values are positive, they win that amount. If the dollar values are negative, they lose that amount. Remember that you are giving advice to teenager and should give advice according to their preferences teenager.
teenager. They
Middle aged
Imagine that you are giving advice to someone middle aged. They are given a prospect and set of sure options. They will compare the prospect to each of the sure options one-by-one. If they reject the sure option, they would play the prospect. If they accept the sure option, they would not play the prospect and receive the sure option. If the dollar values are positive, they win that amount. If the dollar values are negative, they lose that amount. Remember that you are giving advice to a middle aged person and should give advice according their preferences as a middle aged person.
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Senior citizen
Imagine that you are giving advice to senior citizen. They are given a prospect and a set of sure options. They will compare the prospect to each of the sure options one-by-one. If they reject the sure option, they would play the prospect. If they accept the sure option, they would not play the prospect and receive the sure option. If the dollar values are positive, they win that amount. If the dollar values are negative, they lose that amount. Remember that you are giving advice to a senior citizen and should give advice according to their preferences as a senior citizen.
Table 14: System prompts for giving advice.
22


Reviews:
[{"review_id": "aBZ5x4O7Jr", "reviewer": "Reviewer_NcHx", "summary": "This paper describes a series of behavioral economic experiments adapted to be run with various LLMs rather than human subjects. The authors do this to probe whether the LLMs tend to show systemic behavioral biases in their decision behavior that is similar to or varies from the sorts of behavioral biases that are well-described in human economic decisions -- inequity aversion, risk and loss aversion, as well as time discounting.\n\nThe authors general find that while LLMs show non-rational biases of various kinds the manner and degree of these may differ from (at least their chosen comparison class) of human responses.", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful review. We are glad you found our work novel and useful in comparing LLM behavior to human behavior. We believe that the ability to leverage existing human studies to quantifiably compare LLM behavior is a core strength of our approach. \n\n**Weakness 1 (premise)**. The reviewer suggests that because LLMs do not have grounding in the physical world, what they “say” may be different than what LLMs “do”, and hence probing the economic “behavior” of LLMs doesn’t really make sense. We agree that this is a valid (and deep!) point, and acknowledged some of these limitations in Section 5. However, we note that there is a rich body of work on treating LLMs as human-like agents in the context of economic/behavioral experiments (e.g., https://arxiv.org/abs/2208.10264  https://www.nber.org/papers/w31122 ), and these (influential) studies have yielded interesting findings and have spurred on much follow-up work. Moreover, we observe that even in the case of humans, in some of the experiments the questions are posed only as hypotheticals; we trust that the answers given by the experimental subjects reflect what they would truly do. From this point of view, we think that our experimental setup (as well as the experimental setup of the aforementioned studies) is not invalid. We nonetheless acknowledge that this point needs to be discussed more thoroughly, and will do so in the next iteration of the paper.\n\n**Weakness 2 (competence test)**. This is a good question. We do not see obvious patterns in the amount of bias as a function of their competence. To clarify further, the competence tests are designed to test specific competencies related to playing each game. For example, the competence test for the ultimatum game tests whether the LLM can do the requisite arithmetic to make an offer. We assume that we cannot meaningfully analyze an LLM’s behavior if the LLM cannot execute the basic reasoning capabilities to play the game. Given this, we agree that it may be hard to make general economic statements about LLMs as a class, or even as a single LLM family, given that an LLM may show competence in one experiment but not in another. However, once the LLM has passed the competence test, we can identify specific behavioral biases in specific versions of LLMs that may have profound impact on deploying these models in decision-making settings. We will add more discussion around this point.", "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": "This is an, as far as I know, novel application of behavioral economic / decision making studies to LLMs. The use of utility function fitting as a formal modeling exercise allows the authors to quantify the ways in which LLM outputs systemically differ (or not) from human responses in the same or similar settings.", "reasons_to_reject": "I'm not sure exactly what this finding tells us?\n\n\"the economic behavior of LLMs\" -- this premise doesn't quite work.... when we ask people for judgements in these experiments those are assumed to reflect behavior that people would actually take. The LLMs have no grounding in the physical world and so it's not clear if the \"behaviors\" they would undergo / action they'd take is the the same as the text responses if a given LLMs were augmented with a grounding or linking function to the physical world \n\nThe categorical cutoff for the competence test -- do the models vary in their behavioral biases as a function of general \"competence\" here or on other evaluation measures? Otherwise hard to make a general statement about LLMs as a class.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LacI5jJ8cO", "reviewer": "Reviewer_zUgL", "summary": "The paper introduces a novel framework for systematically evaluating the economic decision-making abilities of large language models (LLMs) through the lens of utility theory- a theoretical framework from behavioral economics. The paper adapts classic experimental games like the ultimatum, gambling, and waiting games to derive utility functions that quantify biases such as inequity aversion, risk/loss aversion, and time discounting in LLMs. The paper further conducted competency tests and then experimented with models that passed the different competency tests for their economic decision-making abilities.\n\nThis paper is well-written; the research design is rigorous and well thought-out. A key strength of this paper is the systematic and rigorous approach of grounding the analysis in established approaches from experimental economics and connecting them to LLMs. That approach demonstrates domain knowledge, further lending credibility to the findings. The paper also experimented with a diverse set of LLMs, highlighting the strengths and limitations of each LLM and how the persona represented in the prompts could influence the results. Overall, the contributions of this paper are strong, and it produces new knowledge that would benefit members of the LLM and alignment community in training LLMs to make sound economic decisions.", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful review. We are glad you found our methods rigorous, comprehensive, and well-communicated. We likewise believe that this work presents a valuable set of tools for designing LLMs for economic decision-making, and we will be releasing an open-source package to ensure that others can easily adopt these tools.\n\n**Question 1 (competency tests)**. Thank you for your question, and we are happy to provide further rationale for the competence test for each game presented in the paper. For the ultimatum game, some LLMs did not pass the competence test because they were unable to do basic arithmetic (i.e. 10 dollars - 6 dollars = 4 dollars). For the gambling game, some LLMs did not pass the competence test because they were logically inconsistent about the gambles they take. For example, an LLM would be incompetent if the LLM was willing to bet 50 dollars at 20% probability, unwilling to bet 60 dollars at 20% probability, and willing to bet 70 dollars at 20% probability. For the waiting game, some LLMs did not pass the competence test because they were logically inconsistent about how long they were willing to wait for a monetary gain. For example, an LLM would be incompetent if the LLM was willing to wait 5 years for 50 dollars, unwilling to wait 10 years for 50 dollars, but willing to wait 25 years for 50 dollars. We will provide more examples of these failure modes and discuss this further in the next iteration of the paper.", "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": "This paper is well written, the research design is rigorous and well thought out. The limitations of the paper are also acknowledged. Above all, this paper will make a valuable contribution to the LLM modeling community around designing LLMs that make sound economic decisions.", "reasons_to_reject": "I do not see any reason to reject this paper.", "questions_to_authors": "Why did some of LLMs pass or fail the competency tests? It would have been interesting to provide a rationale for this result.", "extra_scores": {}}, {"review_id": "EDrFmySbc1", "reviewer": "Reviewer_hjkh", "summary": "The paper presents an LLM evaluation study of LLM’s economic decision-making abilities through utility theory. In particular, LLMs generally exhibit stronger inequity aversion, stronger loss aversion, weaker risk aversion, and stronger time discounting compared to human subjects.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful review. \n\n**Weakness 1 (technical depth)**. We present a novel set of benchmarks and evaluation protocols - a topic of interest for COLM. We claim that our methods enable further studies into what impacts economic decision-making abilities, as shown in Section 4. For example, we show how our evaluation protocols can be used to study how implicit assumptions like age group affect an LLM’s risk and loss aversion, enabling researchers to probe for proxies that impact economic decisions. We focused on prompting techniques as the most economically interesting/competent models (i.e., GPT-4) were only accessible via prompting. However, we believe that the framework we present could be used to provide feedback to LLMs during fine-tuning, and such a technique could become practically relevant as more capable open-source models are released. \n\n**Weakness 2 (reasoning and decision-making)**.  Mathematical reasoning is closely related to rationality of economic decision-making insofar as it provides a set of tools necessary to analyze and model economic decisions. For example, the decision for whether or not to take an economic gamble (on a stock, insurance plan, or new home) relies on mathematical reasoning to calculate the expected value over time. However, human economic decision making is not rational in many cases, and some of the economic experiments that we consider in the study were originally designed to surface this irrationality.", "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": "1. The study is novel and interesting\n2. The paper is well-written and clearly organized.\n3. The experiment results are interesting.", "reasons_to_reject": "1. The technical depth of the paper is not enough. The paper simply presents LLM evaluation without (1) developing methods to understand what impacts LLM’s economic decision-making abilities or (2) teaching LLM to make rational economic decisions. \n\n2. It would be beneficial for the reader if the author could clarify the association between LLM’s mathematical reasoning ability and economic decision-making abilities. This will help to understand the research's focus and implications more clearly.", "questions_to_authors": "Can you provide clarification to the weaknesses mentioned above?", "extra_scores": {}}, {"review_id": "HNFt9gF5zc", "reviewer": "Reviewer_Rvzo", "summary": "This paper uses the set up of common behavioral economic games to attempt to measure four properties of LLMs: inequity aversion, risk aversion, loss aversion, and time discounting. That is, using a standard prompting and decoding setup, the authors ask models to make decisions about things like how much money to offer another a player (or whether to accept such an offer) in various hypothetical scenarios. As a precursor to this step, the authors attempt to assess the models' \"competence\", and then only test a subset of the models that pass this first step. The authors demonstrate that for at least some of these agent properties, such as risk aversion, a subset of models end up falling between typical (?) human behavior and \"perfectly rational\" behavior, whereas in others they differ from both.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": true, "final_justification": "", "rebuttal": "Thank you for your thoughtful review.\n\n**Weakness 1 (experimental lightness)**. We would like to push back on this point, as we performed prompt ablations (Appendix B.3, C.3, D.3) on the system and user prompts. For most ablations, we found that the formulation of the prompts had little effect on the strategic choice given repeated samples of the learned distribution. We attempted as much experimental rigor as possible within a limited research budget of compute and API credits. We will make this point clearer in the next iteration of the paper.\n\n**Weakness 2 (competence tests)**. The competence tests are designed to test specific competencies related to playing each game. We assume that we cannot meaningfully analyze an LLM’s behavior if the LLM cannot execute the basic reasoning capabilities to play the game. (We also tried various prompt ablations to ensure that the competence tests were robust). Indeed, our tests are meaningful only if we (at least weakly) assume that LLMs “are understanding these texts like people do”.  While this is a valid critique, this assumption is implicit in pretty much every prompt-based interpretability work, which is one of the only means by which we can probe the most capable LLMs.\n\n**Weakness 3 (presentation)**. We will explicitly explain variables in the accompanying captions for each figure and update plots to be differentiated by pattern in addition to color.\n\n**Weakness 4 (human results)**. Human results are taken from (classic) studies in experimental economics; we did not run our own human studies. We report parameters from papers cited in each game (Fehr & Schmidt 1999, Kahneman & Tversky 1992, Rachlin et al 1991 respectively); we will clarify how we compare human behavior in Section 3 and explicitly cite each value in figure footnotes. We believe the human results are comparable because we directly adapt the experiments reported in these papers to LLMs. \n\n**Weakness 5 (personas)**. Section 4 was intended as a case study, and we intend to further examine the effect of personas and role-playing in follow-on work. The main contribution of our paper is to show that utility theory provides interesting tools for probing the economic behavior of LLMs. Whether and how LLM behavior can be changed through prompting (e.g., roles-based prompting) or finetuning is an interesting avenue for future work.\n\n**Weakness 6 (language choice)**. “Understand” and the ELIZA anecdote has unintended implications and will update accordingly.", "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": "Although this paper doesn't make the case very strongly, the question of how LLMs will behave in various decision-making scenarios that test rationality is clearly important for many hypothetical downstream applications. \n\nThis paper makes what seems like a reasonable first pass attempt at this area, using a combination of well-established behavior economic games, and testing a wide variety of models. \n\nThe authors also include some experiments in which they try to modify the behavioral characteristics of models via prompting, which is a natural extension of the first part of the paper.", "reasons_to_reject": "Although this paper is interesting, there are a few issues with it, and my sense is that it could probably benefit from another round of revisions. \n\nThe most basic issue is the relatively lightness of the experiments. As far as I can tell, the authors just tested a single overall prompt design, and no justification is given for why this particular wording was used, or how sensitive these results might be to minor variations (of which there are innumerable possibilities). \n\nIn addition, the authors' use of a \"competence\" test seems strange, given that this seems to be yet another basically single prompt setup that may or may not relate to the substance of the game. (It clearly does if we assume that models are understanding these texts like people do, but that is not a well justified assumption). Relying on such a narrow assessment to filter out models seems odd, both when it's not clear why models need to be filtered out, but also when there are so many benchmarks on performance that might be used as proxies.\n\nA second issue has to do with presentation. Although the text is clear, the results are not presented in a way that has been streamlined for legibility to an audience that may or may not be familiar with these economic games. For example, Figure 2 contains a table with three columns that can only be interpreted by finding the meanings of symbols in the text; similarly for Figure 3, with no attempt to provide any interpretation of the figures for the reader in the captions. Much of the text in the figures is too small to be able to read clearly, and some of the colors used are hard to distinguish. In Figure 1, the response of \"I offer $4\" seems like a mismatch from the actual experimental setup, which has the potential to mislead people about how precisely the models are engaging in these games.\n\nThird, I might have missed this, but it's not clear to me where the \"Human\" results are coming from? Perhaps these are purely theoretical or based on some sort of existing model, but it doesn't seem like the authors ran new experiments with people using identical setups as for the models, making me wonder if they are directly comparable.\n\nFourth, although the authors make a minimal attempt to explore the idea of personas or role-playing for models, much more could be done in this direction. For example, do we see patterns of responses that are consistent with real people as you change demographic characteristics such as age (to use one example the authors highlight).\n\nFinally, some of the language in this paper is somewhat poorly chosen. The idea that the authors' competence test demonstrates that the a model \"understands\" the prompt seems like a bit of a leap. (By contrast, the authors don't grapple with the idea that models might understand the setting too well; that is, what is \"rational\" for a LLM playing a hypothetical game might differ from that of a person). The authors final comment about the \"fabled relationship between the expert system ELIZA and the author's assistant\" is a bit tone deaf, especially given Weizenbaum's extensive future thoughts about this topic.", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper is part of a growing body of work that treats LLMs as experimental subjects equivalent to humans, and tests their 'cognitive' biases and behaviours in much the same way as existing work in behavioural economics tests that of humans. 

I think there are significant methodological questions about this whole research agenda, because it presupposes that LLMs have some kind of stable 'persona' that is being evaluated through the test, rather than being able to simulate different personae depending on how they are prompted and finetuned. Any lessons learned about their behaviour are really lessons about the default 'persona' induced through post-training by the LLM developers at a particular time, and tell us very little about how the LLM would behave in any practical setting, on the assumption that in any practical setting the user is likely to customise the model's persona, and of course the LLM developers themselves may be assumed to continue intervening in various ways on the default persona. 

In an important sense, then, I actually disagree pretty robustly with the entire premise of the paper! However, reviewers etc far too often take that to be a sufficient reason to reject—it isn't! Though I think the research agenda is misguided, the paper is an excellent articulation of that research agenda: it is, as the reviewers agree, well-written, empirically robust, and informative. My criticisms aren’t a reason to reject the paper, but rather a reason for the authors to reflect further on precisely what it is they take themselves to be showing, and why it matters, as well as grounds for other people to write other papers that ask those methodological questions more directly, critically evaluating this research agenda accordingly. in other words, it's a reason for the authors to sit down with some philosophers of social science and have a good chat about the whole project. Which hopefully they'll be able to do at COLM.
Decision: Accept