# Large Language Models for the Simulation of Human Behavior

Nolwenn Bernard, Krisztian Balog, Saber Zerhoudi, Chengxiang Zhai

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Nolwenn Bernard, Krisztian Balog, Saber Zerhoudi, Chengxiang Zhai. Large Language Models for the Simulation of Human Behavior. 2026. hal-05484009

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Large Language Models for the Simulation of Human Behavior

NOLWENN BERNARD, TH Köln, Germany

KRISZTIAN BALOG, University of Stavanger, Norway

SABER ZERHOUDI, University of Passau, Germany

CHENGXIANG ZHAI, University of Illinois at Urbana-Champaign, USA

Recent advances in large language models (LLMs) have unlocked new possibilities for simulating human behavior across a variety of domains. In computer science, LLM-based user simulation is increasingly applied to the evaluation and training of search engines, recommender systems, and conversational assistants, enabling scalable, controllable, and reproducible interaction testing without direct human involvement. Parallel to these developments, researchers in the social and behavioral sciences have begun to employ LLMs as synthetic populations, agents, or role-playing participants to study decision-making, group dynamics, persuasion, and other cognitive and social phenomena. This survey reviews and synthesizes the emerging literature on LLM-driven simulation of human behavior, spanning both computer and social science perspectives. We highlight publicly available toolkits and frameworks that facilitate reproducible research. We further discuss validity concerns associated with replacing or augmenting human data with LLM-generated data. Finally, we identify open research challenges, outlining a roadmap for future research in this field.

CCS Concepts: • **Computing methodologies** → **Natural language processing; Modeling and simulation; Information systems; Applied computing** → **Law, social and behavioral sciences;**

Additional Key Words and Phrases: User simulation; Social simulation; Artificial intelligence; Large language models

**ACM Reference Format:**

Nolwenn Bernard, Krisztian Balog, Saber Zerhoudi, and ChengXiang Zhai. 2025. Large Language Models for the Simulation of Human Behavior. *ACM Comput. Surv.* 1, 1 (January 2025), 35 pages. https://doi.org/XXXXXXX.XXXXXXX

# 1 Introduction

In society, humans interact with each other as well as with various computer systems in diverse contexts. These interactions are driven by human behavior, which encompasses a wide range of cognitive, emotional, and social processes [125]. Understanding human behavior is essential to designing better systems, improving communication, and informing policies for various applications [125]. However, studying human behavior via real-world experiments poses several challenges, including high costs, scalability issues, ethical concerns, and reproducibility issues [10, 90]. To address these challenges, researchers have employed simulation techniques that allow for controlled, repeatable, and scalable experiments. Moreover, simulation enables counterfactual pilot studies to explore numerous hypothetical scenarios, thereby helping to prioritize the most promising scenarios for real-world experiments.

Authors' Contact Information: Nolwenn Bernard, TH Köln, Cologne, Germany, nolwenn.bernard@th-koeln.de; Krisztian Balog, University of Stavanger, Stavanger, Norway, krisztian.balog@uis.no; Saber Zerhoudi, University of Passau, Passau, Germany, saber.zerhoudi@uni-passau.de; ChengXiang Zhai, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, czhai@illinois.edu.

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Simulation of human behavior has been investigated in various fields, spanning interactive intelligent systems as well as social and behavioral sciences. Notably, these different communities often work in silos, with limited collaboration, and use different terminologies. For example, in the context of interactive intelligent systems, “user simulation” often refers to the simulation of user interactions with a system (e.g., dialogue systems and recommendation systems) from a system-oriented perspective. Conversely, in social and behavioral sciences, “human simulation” often refers to the simulation of interactions among individuals, groups, or society at large from a more human-centric perspective. This disciplinary divide is also reflected in existing surveys on human behavior simulation. For example, among recent surveys, some focus on recommendation systems [89, 133, 136] or conversational systems [85], while others focus on social simulations [81, 106, 110, 116]. However, despite differing application context, the underlying objectives and techniques share significant commonalities and face similar challenges. A primary goal of this survey is to employ a general conceptual framework to examine these commonalities and challenges.

Recently, large language models (LLMs) have unlocked new possibilities in many fields of research and applications, including simulation experiments. For example, LLMs can be employed in digital twin simulations to manage and optimize complex systems [35] and simulations of human behavior in various contexts, e.g., [6, 87, 96]. This survey focuses on the latter; specifically, it aims to provide a comprehensive overview of LLM-based approaches for simulating human behavior along with the resources available to support research in this area. Unlike previous surveys, we investigate this topic from a multi-disciplinary perspective with the objective of bridging the gap between different communities by providing a mapping of existing work based on our proposed framework. This mapping serves two main purposes. First, it offers guidance to relevant work across different fields for any researcher or practitioner interested in applying LLM-based human behavior simulation for specific use cases. Second, it identifies key challenges and potential opportunities for the development of more realistic and effective human behavior simulation approaches, which can serve as a roadmap for multi-disciplinary research on this topic. To achieve this, we aim to answer the following research questions across the fields of interactive intelligent systems and social and behavioral sciences:

**RQ1** For what purposes is LLM-based human behavior simulation employed?

**RQ2** How do approaches to building LLM-based human behavior simulators compare across these fields?

**RQ3** What are the different use cases of LLM-based human behavior simulation?

**RQ4** What approaches have been applied to validate LLM-based human behavior simulation?

**RQ5** How can researchers select appropriate resources for specific research needs?

**RQ6** What limitations and challenges have been identified in previous work?

To answer these questions, we collected and reviewed papers from the fields of interest that study human behavior simulation using LLMs. The selected papers were placed in our conceptual framework for human behavior simulation, which consists of three dimensions: (1) Application context, (2) Aspects of human behavior to be modeled, and (3) Simulation scope and complexity.

Our survey reveals that LLMs represent a paradigm shift from traditional simulation approaches by offering a generalized solution for modeling complex cognitive and social dynamics without extensive task-specific engineering. While the primary motivations—cost reduction, risk mitigation, and data synthesis—remain consistent with historical simulation goals, LLMs uniquely enable the modeling of nuanced linguistic and decision-making behaviors in open-ended contexts. This is highlighted by the uptake of personas, including latent psychological variable, and richer modeling of internal states. However, the flexibility of LLMs introduces novel challenges absent in traditional formal models, such as hallucinations, prompt sensitivity, and difficulties in grounding simulations in real human behavior
<figure><img src="image_2.png" /><figcaption>Fig. 1. In *classic simulation* a real human interacts with a simulated environment (digital twin) that is modeled after a real environment.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. In *user simulation* a simulated user (behavioral twin), modeled after a real human, interacts with an intelligent system.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. In *social simulation* simulated humans, modeled after a real population, interact with each others or with a fully simulated environment, modeled after a real environment (including for example intelligent systems and social media platforms).</figcaption></figure>

data (Section 4.7). Furthermore, our analysis of validation methods (Section 4.5) reveals a fragmented landscape. Unlike established metrics in specific sub-fields, LLM-based simulation currently lacks standardized protocols for ensuring fidelity. To complement our analysis, we provide a curated list of resources (Section 7) mapped to our framework’s three dimensions to help researchers navigate these challenges and select appropriate tools for their specific constraints. We conclude by identifying multiple challenges for future research, particularly, how to design interpretable architecture and specialized LLMs for developing personalized simulators grounded on real human data, how to validate human simulators, and how to develop a shared infrastructure for collaborative research on human simulation.

To summarize, the main contributions of this work are fourfold. First, we introduce a conceptual framework for human behavior simulation designed to characterize and compare diverse approaches across multiple research fields. Second, we provide a comprehensive review of LLM-based human behavior simulation approaches spanning the fields of interactive intelligent systems and social and behavioral sciences. Third, we summarize existing resources supporting research in this field, including guidelines for selecting appropriate resources based on specific research needs. Finally, we discuss open challenges and outline opportunities for future multi-disciplinary research on this topic.

## 2 Background and Foundations

Simulation has been applied in various fields for decades [71]. In this work, we distinguish between three primary categories of simulation.

First, there is the simulation of *non-human systems and phenomena*, which typically involves modeling specific physical, biological, or economic processes. The simulator is designed to replicate the behavior of a system or phenomenon based on its underlying principles and dynamics to predict outcomes or optimize performance, e.g., simulating air flow over an aircraft to optimize aerodynamic performance or market dynamics to inform economic policy decisions.
<table><caption>Table 1. Key terms and definitions related to human behavior simulation in fields reviewed.</caption><thead><tr><td>Term</td><td>Field</td><td>Definition</td></tr></thead><tbody><tr><td>User model</td><td>Interactive Systems</td><td>Model representing the user's characteristics, preferences, and behaviors that may be updated over time [28, 92]</td></tr><tr><td>User simulation</td><td>Interactive Systems</td><td>Mimicking how a user interacts with a system by executing a user model [10, 109]</td></tr><tr><td>Human behavior modeling</td><td>Social and Behavioral Sciences</td><td>General-purpose computational agent that replicates human behavior across domains (e.g., social or political) based on a model</td></tr><tr><td>Social simulation</td><td>Social and Behavioral Sciences</td><td>Computational testbed for social experiments [90]<br>Use computational models as surrogates for human respondents in social science tasks [8]</td></tr><tr><td>Silicon participant</td><td>Social and Behavioral Sciences</td><td>Simulated users [8, 90]</td></tr></tbody></table>

Second, the simulation involves humans interacting with a *simulated environment*. In this scenario, the simulated environment is built based on a real-world counterpart, often referred to as a *digital twin* [37]; see Figure 1. Examples of this type of simulation are flight simulators or virtual reality environments for heart surgery training.

Third, the *simulation of human behavior* refers to the use of models to replicate and predict human actions, decisions, and interactions in various contexts. It includes simulating individual behaviors, group dynamics, social interaction, internal thoughts and feelings, information needs, and how choices are made. In this case, the simulator is designed to act as a proxy for a human or a group, with the goal of producing behavior plausible and consistent with human data or theory. Analogous to the digital twin in the previous category, we can refer to the simulator as a “behavioral twin.” The simulator can have a role, beliefs, goals, and changeable mental states that are altered by interaction. Within this category, two distinct scenarios are distinguished based on the focus of interaction. *User simulation* involves a simulated user interacting directly with an intelligent system (Figure 2). For example, simulated users interact with a search engine to evaluate it. While, *social simulation* extends beyond user-system interactions to encompass interactions between simulated humans themselves or within a fully simulated environment (Figure 3). Some examples are simulating citizens in a virtual society to study opinion dynamics or the impact of new tax policies. Unlike user simulation, which often targets the evaluation of a specific system artifact (e.g., a recommender system), social simulation studies the emergent behaviors, collective dynamics, and social phenomena that arise from agent-to-agent interactions.

For this survey, we consider the *simulation of human behavior* in the fields of interactive intelligence systems and social and behavioral sciences. Human behavior simulation has been applied to diverse application contexts for multiple purposes in these fields. This has led to varied terminology and definitions for this topic. Therefore, in Table 1, we summarize key terms and definitions related to human behavior simulation across the different fields. In the remainder of this survey, the terms “user simulation,” “human behavior simulation,” and “simulation” are used interchangeably to refer to the simulation of human behavior in general, regardless of the specific research field.

Historically, human behavior simulation has served different purposes. In intelligent interactive systems, simulation of user behavior has mainly been used for: (1) training computer systems, (2) evaluating computer systems, (3) modeling users, and (4) generating synthetic data. In contrast, in social and behavioral sciences, simulation is often used for: (1) studying human behavior, (2) testing hypotheses, and (3) informing policy decisions. Furthermore, we note that simulation can be performed at different levels. Indeed, the simulation of individual or group behavior may focus on specific behavioral aspects (e.g., clicking a link or purchasing a product) or on entire processes (e.g., establishing and
maintaining social relationships or evacuating during a disaster). Considering these points, we propose a conceptual
framework for characterizing human behavior simulation in Section 4.1.

Approaches to simulate human behavior have evolved over time. The earliest ones relied on rules and heuristics that guided the simulated behavior depending on the current context and user state; where the rules were manually defined by experts or learned from data (e.g., [27, 43]). From a theoretical perspective, the user's decision-making process can be formalized a Markov Decision Process (MDP), where the user acts based on their internal state (goals, constraints) and the current context [97]. Advancements in machine learning have supported the development of data-driven simulation. Statistical models learn behavioral patterns from a dataset comprising human interactions [5], such as the transformer-based simulator [62]. These models are more flexible than rule-based approaches, as they can simulate human behavior in unseen situations. While offering higher fidelity than rigid rules, they are often less interpretable and controllable than rule-based simulators, and require access to sufficiently large and representative datasets.

More recently, LLM-based simulation has emerged as a flexible solution. This might be explained by the fact that LLMs exhibit capabilities that are relevant to simulation tasks and require little to no domain-specific training data, relying instead on their extensive pre-trained knowledge. In particular, their ability to process and generate human-like text, follow instructions, and learn from in-context examples [15] enables parametrized role playing. In the context of simulation, the LLM-based simulator plays the role of a human with specific characteristics, goals, and states in a given context, e.g., [93, 127, 140].

Given the current trend in human behavior simulation, the scope of this survey is narrowed to LLM-based simulation.
In Sections 5, 6, and 7, we discuss how these LLMs have been exploited in the literature to simulate different aspects
of human behavior in various application contexts. While LLM-based simulation has shown promising results, some
limitations and challenges have been identified, which we discuss in Section 4.7.

3 Literature Review Methodology

In this section, we describe the methodology used to select relevant work for our survey. First, we define the scope of our
survey, including the keywords used for our search queries, as well as the inclusion and exclusion criteria (Section 3.1).
Subsequently, we present the databases used to search for relevant literature in Section 3.2.

3.1 Scope

The scope of this survey is the simulation of human behavior based on LLMs. Specifically, we focus on the fields of
interactive intelligent systems and social and behavioral sciences. To find relevant papers, we defined search queries that
include keywords related to both LLMs and human behavior simulation. The former set simply includes “large language
model” and “LLM” (and their plural forms). For the latter, we built a vocabulary based on the definitions presented in
Section 2, it comprises the following keywords: “user simulation,” “user model,” “user profile,” “user modeling,” “user
profiling,” “human behavior modeling,” “human behavior simulation,” “social simulation,” “synthetic user,” “simulated
user,” “simulated participant,” “silicon participant,” and “silicon sample” (and their plural forms when applicable). By
combining these two sets of keywords, we constructed various search queries to ensure a broad coverage of the topic.
The results of these queries are filtered based on the inclusion and exclusion criteria defined below.

*Inclusion Criteria.* A paper is included if: (i) LLMs are used as one or more core components to simulate human behavior; and (ii) it relates to interactive intelligent systems or social and behavioral sciences. For resources, we add
<table><caption>Table 2. Source databases used for literature search and the research fields of interest they cover.</caption><thead><tr><td>Source</td><td>Fields of interest covered</td><td>URL</td></tr></thead><tbody><tr><td>ACM Digital Library</td><td>Computer Science</td><td>https://dl.acm.org/</td></tr><tr><td>IEEE Xplore Digital Library</td><td>Computer Science</td><td>http://ieeexplore.ieee.org</td></tr><tr><td>Scopus</td><td>Multi-disciplinary</td><td>https://www.scopus.com/</td></tr><tr><td>Google Scholar</td><td>Multi-disciplinary</td><td>https://scholar.google.com/</td></tr><tr><td>arXiv</td><td>Computer Science, Economics</td><td>https://arxiv.org/</td></tr><tr><td>GitHub</td><td>Implementations</td><td>https://github.com/</td></tr></tbody></table>

the following inclusion criteria: (iii) offer a publicly accessible implementation or API for repeatable experiments; and (iv) be sufficiently documented for basic usage.

*Exclusion Criteria.* A paper is excluded if it: (i) defines simulation differently from Section 2; (ii) states that human behavior simulation is not the focus of the paper; (iii) covers human behavior simulation but does not use LLMs; or (iv) is not accessible in full-text. For resources, we add the following exclusion criteria: (v) no functional code or API is accessible; or (vi) support only pre-LLM era simulators.

## 3.2 Databases

Based on the analysis of academic search engines by Gusenbauer and Haddaway [40], the topic of our survey, and the access available to us, we selected six databases for our literature review, as presented in Table 2. ACM Digital Library and IEEE Xplore Digital Library are two major databases for computer science research. In addition to computer science, Scopus also covers social and behavioral sciences. These three databases comprise peer-reviewed publications only. As the topic of our survey is relatively new, we also include Google Scholar and arXiv to cover preprints and technical reports, despite the lack of peer review. Finally, to report on the resources available to support research on human behavior simulation using LLMs, we searched GitHub for open-source implementations and frameworks associated with the relevant literature. During the review process, we also identified and included additional relevant works cited within the collected articles.

## 4 Overview of Simulation of Human Behavior using LLMs

This section addresses the research questions posed in Section 1 by synthesizing the literature reviewed through the lens of our conceptual framework for human behavior simulation (Section 4.1). It offers a summary of the main insights for readers seeking a high-level perspective without reading the detailed sections (Sections 5-7).

### 4.1 A Conceptual Framework for Simulation of Human Behavior

To facilitate the organization of relevant research work, we propose a general conceptual framework for the simulation of human behavior. We characterize simulation along three closely related but orthogonal dimensions: (1) Application context, (2) Aspects of human behavior to be modeled, and (3) Simulation scope and complexity.

While the framework presented here is used to review LLM-based approaches, it is not restricted to them and can be potentially useful for reviewing other approaches to human behavior simulation. The three dimensions are defined as follows:

(1) **Application context:** The environment or domain where the simulated interaction takes place.

* Interactive Intelligent Systems
* Behavioral Economics, Psychology, and Survey Research

* Education

* Clinical Psychology and Health

* Political Science, Public Policy, and Communication

(2) **Aspects of Human Behavior Modeled:** Specific human characteristics, states, and processes being simulated.

* Decision Making

* Information Needs

* Knowledge Level

* Internal States: Affective (frustration, satisfaction, curiosity, etc.), Cognitive (cognitive load, trust, etc.)

* Reasoning

(3) **Simulation Scope and Complexity:** Technical parameters and depth of the simulation itself.

* Temporal Scope: Single action, Entire task, Multiple sessions

* Granularity of Action: High-level (e.g., purchase product or not) vs. Low-level (e.g., exact sequence of clicks)

* Statefulness: Static vs. Dynamic (does the simulated user's state remain fixed or evolve?)

* Persona Complexity: Simple (e.g., "novice user") vs. Complex (defined by demographics, personality traits, specific goals, etc.)

Below, we use these three dimensions to summarize the main findings from the surveyed literature (Sections 5–7). These findings are organized to address our six questions: (1) for what purposes LLMs are used to simulate human behavior in different fields, (2) how the design of simulators differs across fields, (3) what use cases LLM-based simulation covers, (4) how the simulators are being validated, (5) how to choose suitable resources for a given study, and (6) what limitations and challenges have been observed. A summary of how the reviewed work maps to the framework is presented in Table 3.

## 4.2 Application Contexts and Purposes (RQ1 and RQ3)

Across the literature, we identify five broad application contexts:

(1) *Interactive intelligent systems* (Section 5), including search engines, recommender systems, and conversational assistants. Here, LLM-based simulators act as users who interact with a system over one or more sessions. The main purposes are to train and evaluate systems, to generate synthetic interaction logs, and to explore design options and failure modes.

(2) *Behavioral Economics, Psychology, and Survey Research* (Section 6.2). In this context, LLMs simulate human participants in classic behavioral tasks and surveys. Simulations are used to study whether known human behaviors and heuristics are predicted or replicated by LLMs.

(3) *Education* (Section 6.3). Here, simulators take the roles of students, teachers, or whole classrooms. The main purposes are to train teachers and teaching assistants, to stress-test educational tools and questions, to generate synthetic learner data, and to study how learning strategies and support methods might play out in practice.

(4) *Clinical Psychology and Health* (Section 6.4). Simulation is used to model interactions between patients and healthcare providers, including multi-sequential decision-making. It serves different purposes, such as providing a training environment for both medical professionals and artificial health assistants and evaluating them.
<table><caption>Table 3. Overview of Human Behavior Simulation using LLMs mapped to the Conceptual Framework.</caption><thead><tr><th>Application context</th><th>Main aspects of behavior (Taxonomy)</th><th>Typical scope and complexity (Taxonomy)</th></tr></thead><tbody><tr><td>Interactive intelligent systems<br>(search engines, recommendation, conversational assistants; Section 5)</td><td>Decision making (queries, clicks, ratings, next actions),<br>Information needs,<br>Knowledge level and preferences (often via personas),<br>Internal states (satisfaction, frustration, patience; often simple),<br>Reasoning (sometimes explicit, e.g., think-aloud or chain-of-thought)</td><td>Temporal scope: full tasks and single sessions, with some work on multi-session use.<br>Granularity of action: low-level (utterances, queries, clicks, ratings).<br>Statefulness: dynamic within a session; some models track longer-term state.<br>Persona complexity: from simple types (novice vs. expert) to richer profiles with traits and interests.</td></tr><tr><td>Behavioral Economics, Psychology, and Survey Research<br>(economics games, social psychology experiments, survey respondents; Section 6.2)</td><td>Knowledge level (often implicit via personas),<br>Decision making (survey responses and action in games),<br>Reasoning (sometimes via chain-of-thought)</td><td>Temporal scope: single tasks or short sequences.<br>Granularity of action: item-level responses (survey items and game moves).<br>Statefulness: commonly static.<br>Persona complexity: moderate to high, with demographics, personal interests, names, personality traits, and title.</td></tr><tr><td>Education<br>(students, teachers, and classrooms; Section 6.3)</td><td>Knowledge level (what is known, unknown, or misunderstood),<br>Decision making (learning strategies, question and hint requests),<br>Internal states (motivation, confusion, confidence; sometimes explicit),<br>Reasoning (step-by-step solutions and explanations),<br>Information needs (learning goals and tasks)</td><td>Temporal scope: full tasks and lessons, sometimes multiple sessions or full courses.<br>Granularity of action: low-level learning actions (answers, hints, edits, dialogue turns).<br>Statefulness: dynamic, with knowledge and sometimes affect updated over time.<br>Persona complexity: moderate to high, including student profiles, teacher styles, and class roles.</td></tr><tr><td>Clinical Psychology and Health<br>(patients, counselors, doctors, and hospital environment; Section 6.4)</td><td>Decision making (diagnosis, question answering, next actions),<br>Internal states (symptoms, cognitive biases),<br>Reasoning (clinical reasoning via chain-of-thought or retrieval-augmented generation),<br>Information needs (coaching goals, getting/making a diagnostic)</td><td>Temporal scope: single to multiple sessions.<br>Granularity of action: low- and high-level actions (e.g., answers, hospital registration, diagnostics).<br>Statefulness: often static for single session and dynamic for multiple sessions.<br>Persona complexity: high, with detailed personality traits, roles, lifestyle habits, health history and conditions, and backstories.</td></tr><tr><td>Political Science, Public Policy, and Communication<br>(voters, citizens, social media users, societies, information spread, and public opinion; Section 6.5)</td><td>Knowledge level (beliefs, opinions, exposure history),<br>Decision making (what to post, share, support, vote for, react to new policy),<br>Internal states (emotions, trust, group attachment),<br>Reasoning (based on others' actions or information),<br>Information needs (information seeking and selective exposure)</td><td>Temporal scope: commonly long-term to observe phenomena over time.<br>Granularity of action: low-level social actions (posts, comments, likes, follows, votes).<br>Statefulness: strongly dynamic, agents update beliefs, opinions, emotions, and ties over time.<br>Persona complexity: high, with detailed roles, traits, and positions in society/network.</td></tr></tbody></table>

(5) *Political Science, Public Policy, and Communication* (Section 6.5). In this context, LLM-based simulation considers public opinion, voting behavior, and the spread of information online. It allows to study large-scale effects in (online) societies.

In all five contexts, LLM-based simulators serve three recurring roles. First, they act as stand-ins for users or participants to reduce cost and risk. Second, they create large synthetic datasets for training or analysis. Third, they provide controlled environments in which researchers can vary conditions and observe the resulting behavior at scale. The concrete use cases differ. For interactive intelligent systems, they include click and query behavior in search, ratings and reviews in recommendation, and dialogue in conversational systems. For social and behavioral science studies, there are simulations of decisions in the Ultimatum Game and Milgram Shock experiment, as well as responses in opinion and consumer preference surveys. Education considers question answering and problem solving. These use cases are also considered in the context of clinical psychology and health. For political science, public policy, and communication,
use cases include the impact of new tax or vaccination policies, the spread of rumors in a society, and the dynamics of echo chambers based on public opinion evolution.

## 4.3 Aspects of Human Behavior Modeled (RQ2)

Our framework distinguishes five main aspects of human behavior: decision making, information needs, knowledge level, internal states, and reasoning. The reviewed work covers these aspects to different degrees in each context.

In interactive intelligent systems, decision making and information needs are central. Simulators decide which query to issue, which result to click, which item to accept or reject, when to stop, and what to say next in a conversation. Knowledge level is often expressed through personas (e.g., novice vs. expert), long-term interests, or inferred preferences. Internal states such as satisfaction, frustration, curiosity, and patience are sometimes modeled, often in a simplistic manner, for example through a maximum number of turns, an intensity level, or an explicit satisfaction score. Reasoning is rarely modeled in detail, although some work uses chain-of-thought prompts or think-aloud data to make the reasoning process explicit.

In behavioral economics, psychology, and survey research, decision making is also a primary focus, as simulators choose actions in games or provide survey responses. As for interactive intelligent systems, knowledge level is often included in the persona description (e.g., educational background, profession) and some work rely on chain-of-thought prompting to model reasoning.

In educational environments, knowledge level is a primary focus, though the need for modeling a whole learner, including particularly non-cognitive states, was also recognized [76]. Simulated students show partial or wrong knowledge, make typical errors, and update their knowledge over time. Decision making relates to learning strategies and choices, such as which hint to ask for or when to move on. Internal states such as motivation, confusion, or confidence are sometimes included. Reasoning appears in step-by-step solutions or explanations, and information needs are tied to learning goals.

In clinical psychology and health, the aspects modeled varies based on the specific role of the simulated human. For simulated patients, the information needs relate to their health concerns to get a diagnosis, treatment, or lifestyle advice. Commonly, the symptoms are part of their internal states and they decide on actions to communicate them to health professionals. For simulated health professionals, decision making is central to provide diagnostics and treatment plans based on patients' information. Their internal states may include cognitive biases that affect their clinical reasoning and decisions.

In political science, public policy, and communication, decision making, knowledge level, internal states, and reasoning are all interconnected and affect the behavior of the simulated humans (e.g., citizens, voters, social media users). The knowledge level mainly relates to beliefs and opinions, in some cases shaped by exposure history. The internal states include emotions, trust, and group attachment. Information needs appear in the form of information seeking and selective exposure. These aspects jointly influence decisions about what to post, share, support, or vote for, as well as how to react to new policies or information.

Across fields, we see a gradual shift from simple “average user” behavior toward richer personas and stateful models that combine several aspects. However, there is no shared standard yet on what a persona should contain or which aspects are essential for a given study.
## 4.4 Simulation Scope and Complexity (RQ2 and RQ3)

The third dimension of our framework describes temporal scope, granularity of actions, statefulness, and persona complexity. The reviewed work occupies this space in different ways.

In many studies, the temporal scope covers an entire task (e.g., one search session, one recommendation session, one survey, or one doctor visit). Some work extends this to multiple sessions and long-term interaction, especially in recommender systems, education, clinical psychology, health, political science, public policy, and communication. Typically, action granularity ranges from single choices or responses (e.g., a rating, a survey answer, or a treatment decision) to fine-grained sequences of actions, such as clicks, queries, posts, or dialogue turns.

Statefulness varies widely. Some simulators are static: the persona and internal state are fixed during the interaction, and there is no long-term memory. Others maintain dynamic states that track evolving knowledge, preferences, emotions, or social ties, especially in social simulation. Memory modules, profiles, and logs of past interactions are used to support this dynamic behavior. Persona complexity also spans a wide range, from simple labels such as “novice” or “expert” to rich profiles that combine demographics, personality traits, interests, health status, and social context.

Overall, we observe different trends across the different application context. Interactive intelligent systems tend to model fine-grained actions and dynamic states within a session, with personas that range from simple to moderately complex. Behavioral economics, psychology, and survey research often focus on moderately complex personas performing short-term tasks with item-level actions and mostly static states. Education simulations emphasize dynamic knowledge and learning actions over lessons or full courses, with moderately complex student and teacher personas. For clinical psychology and health, the trend is towards high persona complexity, dynamic states, and mixed granularity of actions over multiple sessions, although static single-session simulations also exist in the literature. A similar trend is observed in political science, public policy, and communication, apart from the granularity of actions that tends to be fine-grained (posts, comments, likes, follows, votes).

We note that LLM-based simulation has particularly streamlined the modeling of complex personas and wide ranges of actions. The description of the persona and specification of actions is often done through natural language prompts, which can be modified and extended with minimal technical overhead. This flexibility facilitates rapid prototyping and experimentation with richer and more diverse simulations compared to traditional approaches (i.e., rule- and neural-based simulators). Additionally, we see a trend toward more dynamic and stateful simulations over longitudinal scopes, in part enabled by the integration of memory modules.

## 4.5 Validation of Simulation (RQ4)

As LLM-based human behavior simulation is becoming more prevalent, it is essential to assess its validity and fidelity. Understanding these aspects enables researchers to make informed decisions and analyses when applying LLM-based simulation in different contexts.

Validation is also a general challenge in human simulation that is not limited to LLM-based approaches [10]. Fundamentally, it relates to the questions: *To what extent does the simulated behavior mimic real human behavior? Is the simulated behavior believable (with regards to specific conditions)?* Multiple approaches and resources have been proposed to answer these questions, considering specific aspects of human behavior or as a whole [10]. Based on the reviewed literature, we identify three main validation approaches used for LLM-based human behavior simulation:

* *Automatic comparison against human data.* The simulated behavior (synthetic interactions) is compared against human data using automatic metrics. Depending on the context, this may involve comparing distributions of actions
(e.g., recommendation ratings and choice proportions), sequence similarity, statistical properties, or task performance. The comparison can also be done based on downstream task results obtained with humans vs. simulated users, based on some measure of task success.

* *Replication of real-world findings/trends.* Evaluate whether the simulated behavior reproduces real-world findings, such as known biases, psychological effects, or behavioral trends observed in prior studies (involving real humans). Recently, the CoMPOsT framework [20] has been proposed to systematically evaluate the susceptibility of LLM-based simulations to caricature specific personas. Additionally, simulator testers [54] offer a generic validation approach, however, they have not been applied to LLM-based human behavior simulation yet.

* *Human assessment.* Human judges are asked to evaluate the realism, believability, or quality of the simulated behavior. The evaluation can be done using pointwise assessment (e.g., Likert scales) or pairwise comparison (e.g., A/B testing between different simulators or in the style of a Turing test to distinguish between simulated and real behavior). This type of validation has also been performed using LLM-based judges instead of humans in the context of interactive intelligent systems.

We note that within each category, there are variations on the protocols and metrics used. This diversity likely stems from the fact that different simulations have peculiarities and do not always require the same degree or type of validity, depending on the application context and use case [95]. For example, even if the simulated behavior is imperfect, it might still be useful for training or identifying trends, e.g., [87]. Nevertheless, we believe that developing global guidelines and best practices for validation would be beneficial to the community, as it would harmonize validation efforts and facilitate cross-simulator comparison in terms of validity.

## 4.6 Resources and Tool Choice (RQ5)

The choice of LLMs, toolkits, and datasets can be guided by our framework. For a given study, one should first identify the application context and the main aspects of behavior that matter. For example, studies on search and recommendation often need detailed decision traces and information needs, while social simulations require support for multi-agent interactions and network structure, with education context specifically requiring fine-grained control over knowledge level and learning trajectories. Next, one should decide on the desired scope and complexity: single-step vs. multi-step, single agent vs. many agents, static vs. dynamic state, and simple vs. complex personas.

Sections 5 and 6 describe concrete simulators that can act as templates for new studies in similar contexts. Section 7 reviews general-purpose toolkits and frameworks that support, for example, multi-agent orchestration, long-term memory, classroom simulation, or human-system interactions. Table 3 summarizes how typical applications map to our three taxonomic dimensions and can help researchers choose suitable starting points for their own work.

## 4.7 Limitations and Challenges (RQ6)

The surveyed literature reveals several core limitations and challenges of LLM-based human behavior simulation. Inherent limitations of underlying LLMs can cascade into the simulation, affecting its validity and reliability. For example, reproduction or amplification of biases from training data can lead to caricatural and potentially harmful behaviors, limited control over generated outputs can result in inconsistencies with the context or information leakage, and excessive agreement can speed up convergence in social simulation. Consequently, grounding simulation in real human behavior and data remains a primary challenge. However, validation confirming the efficacy of grounding solutions remains missing or limited in many studies. Another limitation relates to the fragmentation of resources to
support LLM-based simulation, hindering interoperability and reusability across application contexts. These limitations and challenges help to identify directions for future research in this area; we present some of them in Section 8.

# 5 LLM-based User Simulation for Interactive Intelligent Systems

Simulation has been investigated in the field of computer science for decades, with applications spanning various subfields [10, 82]. Recent advancements, especially the emergence of LLMs, have sparked a resurgence of interest in this area. In this section, we particularly focus on the literature on LLM-based simulation in the context of interactive intelligent systems. Accordingly, we consider application contexts involving direct human-system interactions. For these application contexts, user simulation aims to model and simulate user interactions with an interactive system [10, 82, 109]. User simulation serves multiple purposes, including guiding design choices, testing hypotheses, evaluating usability, training intelligent systems, and assessing system performance, all in a scalable, reproducible, cost- and time-efficient manner compared to traditional user studies involving real users [10, 82]. In general, user simulation is considered as a complementary approach to traditional user studies, rather than a replacement [10, 82]. Crucially, it provides insights during the early stages of system design and development without putting real users at risk, allowing researchers to improve or filter out poor systems before conducting targeted user studies with real users. For example, UXAgent [70] simulates user interactions with web interfaces to evaluate their usability and guide a designer's decisions prior to user studies, while SimUser [117] provides usability feedback to improve mobile application prototypes.

We acknowledge that LLM-as-a-Judge approaches [144], which are being increasingly used to evaluate outputs from interactive intelligent systems, can be considered as a form of user simulation. Arguably, they simulate the behavior of a human assessor rating the system outputs. However, we choose to focus on LLM-based user simulation where the main purpose is to simulate dynamic user interactions with an interactive intelligent system. Therefore, we refer the readers interested in LLM-as-a-Judge approaches to dedicated surveys on this topic, e.g., [57, 58].

In this section, we begin by presenting the most common approaches to build LLM-based user simulators in this context, alongside identified validity concerns. Then, we discuss the core applications, construction techniques, and validation approaches for three types of interactive intelligent systems distinguished in the reviewed literature: search engines, recommendation systems, and conversational assistants. We note that there is a certain degree of overlap between these different types of systems. However, the possible user actions and the nature of interactions often differ depending on the type of system.

## 5.1 Methodology

Formally, user simulation for interactive intelligent systems can be defined through the User-System-Task framework [10]. In this framework, three core variables influence simulated behavior:

* Task (*T*): The objective a user intends to accomplish, which varies in complexity and required interaction strategies.

* System (*S*): The interactive system that dictates the set of possible actions *A* available to the user.

* User (*U*): The individual characteristics—such as age, proficiency, preferences, and cognitive style—that lead different users to behave differently for the same task.

The resulting computational problem is to construct an agent capable of generating a valid sequence of actions $A$ that faithfully mimics how a specific user $U$ would attempt to complete task $T$ using system $S$.

Prior to the advent of LLMs, approaches to solving this problem in interactive intelligent systems mainly relied on rule-based approaches, e.g., [27, 97, 98], often formalized via Markov Decision Processes (MDPs). A smaller number
of studies also proposed data-driven approaches based on deep learning models, e.g., [62, 134]. However, the demand
for more adaptable and cost-efficient user simulation has led to the exploration of LLMs. LLMs offer significant
flexibility, adapting to multiple scenarios (T, S) and personas (U) with reduced manual effort and data requirements
compared to previous approaches.

In the current literature, we identify two main architectures used to build these simulators: single-LLM and modular architectures. The former considers a standalone LLM that can be prompted or fine-tuned to simulate user behavior directly, e.g., [4, 107, 132]. The latter takes a more fine-grained approach by leveraging distinct modules (often powered by LLMs) with assigned roles and objectives, e.g., [39, 147]. Typical modules include a profile module to represent user characteristics and preferences (U), a memory module to store interaction history, and an action module to determine the next user action based on the context and user state.

Despite the recent advancements and promising results demonstrated by LLM-based user simulation, several validity concerns have been raised. Empirical results have shown that the behavior of LLMs can diverge from the persona or objective described in the context [85, 118, 122]. This divergence may stem from the difficulty of rigorously controlling LLM outputs, which are influenced by prompt design and inherent biases present in the training data. Variations in prompt detail can lead to different behaviors, such as data leakage, reduced diversity, and hallucinations [52, 122, 146]. Another concern relates to the limited consideration of the multi-dimensional context of human-system interactions (physical, social, cultural, emotional, etc.) [82]. While these dimensions significantly influence user behavior at various levels, their integration into current simulation models remains limited.

## 5.2 Search Engines

LLM-based simulation is primarily being used to model user interactions with search engines to evaluate their performance. Simulating search sessions addresses the challenge of collecting large-scale user interaction logs while respecting user privacy.

Core applications:

* *Replicating Search Sessions:* Simulating the sequence of user actions taken during a search session to satisfy an information need and assess the system’s performance [93, 131, 132]. User actions include query formulation, search results page examination, clicking on retrieved documents, and stopping the search session [11, 108, 128].

* *Generating Synthetic Queries:* Simulating queries that a user would formulate to express an information need. LLM-based simulation has been used to generate query variants [3, 4, 29] and predict the next query in a session [16, 24] given a context.

Techniques for realistic simulation:

* *Prompt Engineering:* Designing prompts using different strategies, from zero-shot [24, 29, 30] to few-shot [4] and chain-of-thought prompting [3]. The prompts typically include instructions and context (e.g., previous queries, topic description, backstory) to guide the LLM in simulating user behavior.

* *Demographic and Persona Prompting:* Conditioning LLMs with specific names, titles, and attribute profiles (e.g., age, income, education) to simulate diverse subpopulations and implicitly capture demographic-specific behaviors [3, 93].

* *Multi-Prompt Decomposition:* Using action-specific prompts to simulate different user actions sequentially during each interaction round [93, 131].

* *Supervised Fine-Tuning:* Fine-tuning LLMs on datasets of real user behavior to better emulate user actions and reasoning processes during search sessions [16, 128, 132].
Validation approaches:

* *Query Similarity Metrics:* Using metrics from natural language generation and machine translation to compare the similarity between real and simulated queries [3, 4, 129, 131, 132].

* *Next Action Prediction Accuracy:* Measuring the accuracy of the simulator in predicting the next user action (e.g., query reformulation, click, stop decision) given a search context, using real user data as ground truth [128, 129, 131, 132].

* *Diversity and/or Plausibility Assessment:* Human evaluation to assess the diversity and/or plausibility of simulated queries [3, 129].

## 5.3 Recommender Systems

Researchers in the field of recommender systems employ LLM-based user simulation to support the training and evaluation of new systems while reducing the reliance on real user data. This approach facilitates the generation of user feedback on recommended items across various user preferences and profiles.

Core applications:

* *Training via Reinforcement Learning:* Simulating user interactions with a recommender system to provide reward signals that guide the learning process of the recommendation policy [64, 138]. While variable, these signals typically corresponds to user feedback, such as clicks, ratings, or reviews, on recommended items.

* *Generating User Feedback for Recommendations:* Employing LLM-based user simulators to assess recommended items based on user preferences and interests. This assessment can be expressed via ratings (binary or graded) and/or reviews [14, 130, 142, 150]. Implicit feedback via clicks on recommended items has also been considered [14, 86].

* *Generating Feedback for User Experience:* Simulating qualitative feedback on user experience with a recommender system through post-session interviews [130].

* *Robustness Testing:* Simulating shilling attacks to evaluate the robustness of recommender systems [111].

Techniques for realistic simulation:

* *Demographic and Persona Prompting:* Conditioning LLMs with specific names, titles, and attribute profiles (e.g., age, occupation, Big Five personality traits [47]) to simulate diverse user profiles and behaviors [14, 39, 111, 130, 142].

* *Incorporating Preferences:* Embedding user preferences and interests into the simulation process to guide the assessment of recommended items and overall behavior during a recommendation session. These preferences are often inferred from user-item interactions in existing datasets [64, 138, 150].

* *Memory Modeling:* Storing previous interactions with the recommender system and its recommendations to inform the decision-making process and recommended item assessment [14, 39, 111, 130]. This may also include emotional states [14, 130] and/or sensory experiences [14, 111].

* *Inter-Module Communication:* Using multiple (LLM-based) modules to model diverse aspects of a user and its behavior, e.g., [14, 39, 111, 130, 142]. Interconnecting these specialized modules enables better coordination, leading to realistic simulation.

Validation approaches:

* *User Action Prediction Accuracy:* Measuring the accuracy of the simulator in predicting user feedback (e.g., ratings, clicks) on recommended items using real user data as ground truth [14, 111, 142, 150]. Wang et al. [115] establish a benchmark using the OPeRA dataset to assess the ability of LLM-based simulators to mimic real users at a given time point in an online shopping session.
*   **Simulation Outcome Comparison**: Comparing the outcomes of recommendation sessions (e.g., recommendation accuracy, diversity) when using real versus simulated user data [14, 64, 130].
*   **Reproducing Real-World Trends**: Assessing the realism of simulated interactions by comparing results obtained with trends observed in the real world [130].
*   **Human-Likeness Assessment**: Evaluating the human-likeness of simulated interactions through pairwise or pointwise comparison using human annotators or LLM-based judges [14, 111].

## 5.4 Conversational Assistants

LLM-based user simulation is widely used in the context of conversational assistants to model user interactions, with a focus on simulating user utterances. The majority of the reviewed literature focuses on task-oriented conversations, where a user is seeking assistance to complete a specific task such as booking a flight, finding recommendations, or learning about a topic. Simulation in this context enables the generation of diverse and contextually relevant utterances and actions that can be used to train and evaluate conversational systems in an interactive setting.

Core applications:

*   **Generating Synthetic Dialogue Datasets**: Simulating multi-turn conversations between users and conversational assistants to create synthetic datasets for training and evaluating conversational systems [51, 78, 149].

*   **Generating Next User Utterance**: Simulating the next user utterance that is contextually relevant and coherent with the ongoing conversation and the user's objectives. Some simulators focus on utterances with specific intents, such as follow-up questions [49] and answers to clarifying questions [101], while others cover a broader range of intents [18, 23, 102, 103, 107, 114, 147].

*   **Training via Reinforcement Learning**: Simulating a user conversing with a digital assistant to provide a reward signal that guides the learning process of the conversational policy. The reward signal can be based on whether the user's objectives are met during the conversation [6].

*   **Shopping Assistance**: Simulating user interactions with a shopping assistant, including actions involving the user interface (e.g., clicking on recommended products and asking related questions about them) [105].

*   **Generating Question-Answer Interactions**: Simulating conversations where the simulator asks questions on a particular topic to which the conversational assistant provides answers [1].

Techniques for realistic simulation:

*   **Prompt Engineering**: Designing prompts using different strategies, from zero-shot [49] to in-context learning [51, 102, 107]. Prompts typically includes instructions, current context, and the user's objective to guide the LLM in simulating user utterances.

*   **Demographic and Persona Prompting**: Conditioning LLMs with specific names, titles, and attribute profiles (e.g., gender, age, patience, Big Five personality traits [47]) to simulate diverse user profiles and behaviors. The values for the different elements of the persona/profile can be inferred from real user data [38, 147], randomly sampled [18, 102, 143, 147], or manually defined [105, 147].

*   **Inter-module Communication**: Using multiple (LLM-based) modules to model diverse aspects of a user and its behavior. Examples of modules include user profile, memory, intent prediction, and utterance generation [18, 147].

*   **Supervised Fine-Tuning**: Fine-tuning LLMs on datasets of real user behavior to learn underlying patterns of user behavior in conversations [103].

Validation approaches:
* *Interaction Similarity with Real User Data:* Assessing the similarity of interaction logs, particularly generated utterances, with real user data [49, 101, 105].

* *Goal Success Rate Approximation:* Comparing the goal success rate obtained with simulated vs. real interactions [23].

* *Survey Alignment:* Comparing survey results obtained from real users and simulated users after interacting with the conversational assistant [105].

* *Turing Tests and Behavioral Distinction:* Pairwise comparison, conducted by human or LLM judges, to distinguish simulated interactions exhibiting specific characteristics [1, 102] and simulated vs. real interactions [49, 101, 114].

# 6 LLM-based Human Simulation in Social and Behavioral Sciences

Social science research addresses questions about how people and societies function, investigating behaviors and interactions within broader social contexts such as policies, institutions, and environments. This field intrinsically benefits from foundational behavioral science research, which focuses on how and why individuals behave the way they do, broadly covering psychological, cognitive, and biological mechanisms behind behavior.

Traditional approaches to social and behavioral science research include theoretical and conceptual analysis, qualitative methods such as interviews, focus groups, and ethnography, as well as quantitative methods such as surveys, controlled experiments, and statistical modeling to identify patterns and causal relationships. However, these methods often require a significant amount of resources and consequently cannot easily scale up to involve large participant samples. They may also not always be feasible, e.g., due to privacy concerns and harmful experiments. Therefore, there has been growing interest in LLM-based simulation to substitute or complement human participants. LLMs facilitate the simulation of individual and group behavior at scale with significant reduction of costs [34]. For example, Binz et al. [13] fine-tuned Llama-3.1-70B on data from 160 human subjects experiments, achieving better performance than existing cognitive models, while virtual societies enable the study of group dynamics and social phenomena, e.g., [32, 60, 87]. Furthermore, LLM-based simulation enables the exploration of counterfactual scenarios [41].

This section examines LLM-based simulation within the social and behavioral sciences. In particular, we synthesize LLM-based approaches for simulating human behavior and report concerns raised by the community. Then, we provide a detailed review of some representative work in specific application contexts, including behavioral economics, psychology, education, healthcare, and political science, focusing on applications, techniques, and validation methods.

## 6.1 Methodology

Methodologies for simulating human behavior in social sciences are evolving from traditional approaches, including agent-based modeling [31], system dynamics [104], microsimulation [61], and network simulation [84], toward LLM-powered social agents, e.g., [81, 112, 113]. Unlike traditional agents constrained by fixed rules, LLM-based agents are far more adaptive, and context-aware, thereby capable of generating human-like responses dynamically [91]. Current LLM-based approaches generally fall into two categories: modeling individual behavior and modeling group behavior and social interactions.

### 6.1.1 Modeling Individual Behavior.
The fundamental unit of social simulation is the individual agent. Methodologies for constructing these agents range from simple conditioning to complex cognitive architectures.

* *Persona Conditioning ("Silicon Samples"):* The simplest approach involves prompting the LLM with specific demographic profiles or backstories to mimic a target sub-population. Argyle et al. [8] propose using reliable external
data to define the demographic distribution $p(B)$ and relying on the LLM only to estimate the conditional response distribution $p(V|B)$, thereby mitigating model bias regarding population statistics.

*   **Data-Grounded Agents:** To increase fidelity, agents can be grounded in rich qualitative data rather than generic personas. For example, Park et al. [88] construct interview-based agents by incorporating full interview transcripts into the agent's context window, allowing the LLM to infer and replicate the specific behaviors and reasoning styles of real individuals.

*   **Cognitive Architectures:** More advanced frameworks, such as Generative Agents [87] and Silicon Participants [90], equip agents with explicit internal modules. These architectures typically include: (1) a *Memory* module to maintain long-term context, (2) a *Reflection* mechanism to synthesize high-level insights from daily observations, and (3) a *Planning* module to direct future actions. These components are often governed by prompts grounded in social or psychological theories (e.g., motivation, emotion).

### 6.1.2 Modeling Group Behavior and Social Interactions.
Simulating groups involves orchestrating interactions between multiple agents within a shared environment.

*   **Aggregated Sampling:** This approach treats LLM-based agents as a virtual survey panel. By aggregating responses from a diverse set of “silicon samples,” researchers can estimate group-level opinion distributions or treatment effects without explicitly modeling direct interaction between agents [8].

*   **Interactive Multi-Agent Environments:** To study emergent phenomena, agents are placed in a shared environment—such as a virtual town [87] or a social media platform [32]—where they can perceive others and perform actions (e.g., posting, reshaping, moving). This aligns with the Multi-Agent Systems paradigm, where LLMs enable agents to adhere to complex social norms and organizational roles.

*   **State-Aware Propagation Models:** Frameworks like $S^3$ [33] hybridize LLMs with formal state models to track internal evolution. For instance, an agent's internal states (e.g., emotion or attitude) can be modeled as a Markov Decision Process that transitions based on exposure to new information, while the LLM generates the resulting content or social action. This allows for rigorous simulation of information and sentiment diffusion across a network.

Similarly to the field of interactive intelligent systems, some concerns have been raised regarding these LLM-based social simulation approach. It includes the limited capacity of LLMs to capture human psychological depth and intrinsic motivation, as well as ethical concerns [112]. Recently, Anthis et al. [7] identified five specific challenges: diversity, bias, sycophancy, alienness, and generalization. Nevertheless, scholars argue that LLM-based social simulations can already be useful for pilot and exploratory studies, with more widespread use being anticipated considering the trajectory of rapidly advancing LLM capabilities [7].

## 6.2 Behavioral Economics, Psychology, and Survey Research

Researchers in behavioral economics, psychology, and marketing are using LLMs to simulate human decision-making in classic experimental tasks and large-scale surveys. This domain explores whether LLM-agents exhibit human-like cognitive biases and heuristics in economic games, consumer choice scenarios, and moral reasoning, while also investigating the methodology of replacing human participants with “silicon samples” to scale social science research. Core applications:

*   **Replicating Behavioral and Moral Phenomena:** Simulating participant behavior in foundational studies such as the Ultimatum Game (economics), the Milgram Shock Experiment (social psychology), and Garden Path sentence
processing (psycholinguistics) to test if LLMs recover known human phenomena [2]. It includes evaluating how LLMs
rate the ethics of various scenarios to compare alignment with human moral intuitions across contexts [25, 100].

* *Consumer, Marketing, and Survey Research:* Generating “silicon samples” to predict consumer preferences, pretest survey items, and evaluate visual stimuli [96]. This includes simulating responses to large-scale public opinion polls [8], financial wellbeing questionnaires [48], and vote prediction surveys [8], to estimate population parameters and biases without extensive human recruitment [45].

Techniques for realistic simulation:

* *Leveraging Human Data:* Enhancing model fidelity by using human data in two key ways: developing specialized models (e.g., CENTAUR) fine-tuned on large datasets of human experimental results to better predict behavior [100], and employing “silicon sampling” to correct for skewed training data by conditioning models on backstories sampled from representative human surveys [8].

* *Demographic and Persona Prompting:* Conditioning LLMs with specific names, titles, and attribute profiles (e.g., age, income, gender) to simulate diverse subpopulations and capture demographic-specific behaviors [2, 8, 48].

* *Parameter and Prompt Tuning:* Adjusting LLM parameters (e.g., temperature) to induce response variability and using few-shot or chain-of-thought prompting to align model outputs with human-like reasoning patterns [8, 96].

* *Adaptive Sample Size Selection:* Using statistical methods to determine the optimal number of synthetic responses needed to construct valid confidence intervals for human population parameters, balancing simulation cost with accuracy [45].

Validation approaches:

* *Replication of Known Effects:* Comparing LLM-generated results against historical human data to see if robust psychological effects (e.g., gender differences in bargaining, framing effects) are reproduced [2, 25, 96].

* *Distributional Analysis:* Comparing the variance and distribution of synthetic responses to human survey data to identify "hyper-accuracy" distortions (where models lack human-like error) or reduced diversity in opinions [2, 48]. Huang et al. [45] construct confidence sets from synthetic data and verify if they accurately cover true human population statistics, thereby measuring the alignment gap between the model and reality.

6.3 Education

The field of education is employing LLM-based simulation of students is to create scalable, safe, and controllable environments for educational training and evaluation without relying on human participants, which can be resource-intensive and ethically complex.

Core applications:

* *Teacher and TA Training:* Simulated students allow pre-service teachers and teaching assistants to practice guiding learners, managing classrooms, and experiencing realistic tutoring scenarios in a low-stakes environment. Simulated users can help new instructors build confidence and practice pedagogical strategies before interacting with real students [59, 77, 79, 140]. Simulations can also help automate other forms of interactive learning, such as learning by teaching, where learners tutor a peer learner (the role played by the AI agent) [46].

* *Evaluating Educational Tools:* LLM-simulated profiles can be used to evaluate the quality of assessment items. This includes predicting student performance to identify questions that need revision [69, 79] and serving as a low-cost 'first-pass' filter to audit vast amounts of AI-generated questions before human testing [66]. Simulation techniques also facilitate the development of intelligent tutoring systems [68].
* *Simulated Classrooms for Peer Learning:* Beyond single agents, entire classrooms may be simulated, including teacher agents and various classmate personas (e.g., “Class Clown,” “Deep Thinker”). This multi-agent approach enhances social presence and engagement for the human user compared to solitary learning [140].

* *Generating Synthetic Educational Data:* LLMs are used to generate synthetic data when real data is scarce or private. This includes generating large datasets of student code containing typical errors [73].

* *Metacognitive Cultivation:* Simulations are being explored to model students with insufficient learning capabilities, who require targeted support [59].

Techniques for realistic simulation:

* *Restraining Knowledge and Competence:* A critical methodology is restraining the LLM’s inherent extensive knowledge to realistically simulate a novice learner. Techniques include “Reflect-Respond” pipelines that force the agent to use only information explicitly taught to it during the session, thereby simulating gradual learning and persistent misconceptions [46].

* *Persona and Profile-Based Prompting:* Simulations often utilize detailed profiles including demographics, psychological traits (e.g., MBTI, Big Five), and specific learning challenges to guide the LLM’s behavior. For evaluating questions, profiles may be defined by which Knowledge Components a student has mastered, is confused by, or has not seen [59, 68, 69].

* *Incorporating Real Student Data:* For complex tasks like generating realistic student code trajectories, fine-tuning models on authentic, timestamped student submissions captures “messy” learning dynamics—such as incremental edits and stylistic variations—better than prompting alone [80]. Providing the model with taxonomies and frequency data of real student errors significantly improves realism [73].

* *Multi-Agent Orchestration:* For classroom simulations, a “Session Controller” or manager agent is used to observe the class state and determine which agent (teacher, assistant, or specific classmate) should speak next, managing the dynamic flow of a group chat without a strict pre-defined workflow [140].

* *Mode-Shifting:* To balance realism with pedagogical effectiveness, agents may use “mode-shifting,” periodically switching from a passive learner role to an active questioner role (asking “why” and “how”) to scaffold the human user’s knowledge building [46].

* *Contextual Cognitive Modeling:* To capture how course materials modulate student learning, Xu et al. [119] introduce a Transferable Iterative Reflection (TIR) module. This mechanism enables agents to iteratively analyze prediction errors and store generalized insights, allowing the simulator to adapt to long interaction contexts and accurately replicate fine-grained learning dynamics and inter-student correlations.

* *Simulation Authoring Framework:* Regulating the behavior of learner simulators by enabling experts to compose simulations from testable hypotheses. Frameworks such as Hyp-Mix [74] allow researchers to modularly combine and evaluate specific behavioral theories to drive learner actions.

Validation approaches:

* *Psychometric Alignment:* A primary validation method is comparing the statistical performance of simulated students against real student data. This includes aligning Item Response Function (IRF) curves to ensure simulated students exhibit realistic probabilities of answering correctly based on difficulty [66, 69], and comparing accuracy and error distribution patterns on benchmarks [68, 73].
*   *Learning Curve Comparison:* For simulations of learning processes, validation involves comparing the error rates of simulated students over successive opportunities to know human learning curves (e.g., the power law of practice) to ensure the agent “learns” at a realistic rate [119].

*   *Turing Tests and Expert Review:* Many studies employ blind pairwise comparisons where domain experts (teachers) distinguish between human and AI student responses [80]. Experts also rate the generated responses on dimensions such as rationality, diversity, and relevance to the specific persona [59, 66, 77], or provide qualitative insights through semi-structured interviews to identify fine-grained behavioral gaps, such as unnatural attentiveness or lack of emotional depth [79].

*   *Pedagogical Efficacy:* Validation also measures the impact on the human user. Studies assess whether interacting with the simulator actually leads to learning gains for the human student [46] or improves the teaching skills of pre-service teachers [77].

*   *Behavior and Belief Consistency:* Evaluating learner simulators by verifying alignment between their observable behaviors and internal beliefs. For example, Mannekote et al. [75] propose validating simulators by systematically checking whether the actions of an LLM-simulated learner are consistent with its stated beliefs and knowledge state.

## 6.4 Clinical Psychology and Health

In clinical psychology and health, LLM-based simulation is being applied to model complex interactions between patients and healthcare providers, moving beyond static question-answering to dynamic, sequential decision-making environments. These simulations facilitate the training of medical agents, the benchmarking of diagnostic capabilities under realistic constraints, and the generation of synthetic data for low-resource domains like counseling.

Core applications:

*   *Benchmarking Agents:* Evaluating agent performance in dynamic environments, ranging from diagnostic accuracy involving sequential reasoning and tool usage [99] to the efficacy of lifestyle coaching for conditions like sleep and diabetes using grounded user simulations [127].

*   *Medical Education:* Providing high-fidelity simulated patients for medical students to practice history-taking and clinical reasoning, verified against human standardized patients [124].

*   *Training Agents:* Enabling doctor agents to autonomously improve their medical expertise through repeated practice and experience accumulation within a virtual hospital environment [60].

*   *Generating Training Data:* Generating large-scale, multi-turn counseling dialogues based on cognitive behavioral therapy to train specialized counseling models in a privacy-preserving manner [56].

Techniques for realistic simulation:

*   *Data-Driven Grounding:* Constructing synthetic user profiles based on real-world datasets, such as longitudinal health tracking data [127], electronic health records [124], or psychological datasets [56] to ensure clinical validity.

*   *Psychological and Behavioral Modeling:* Incorporating established frameworks like the COM-B model for behavioral barriers [127], the Big Five personality traits [124], and specific cognitive distortions [56] to simulate realistic patient behaviors and mental states.

*   *Multi-Agent Architectures:* Distributing roles among specialized agents (e.g., Patient, Doctor, Measurement, Moderator) to simulate the full clinical workflow, from triage to diagnosis and follow-up [60, 99, 124].

*   *Modeling Biases and Tool Use:* Explicitly modeling cognitive and implicit biases (e.g., recency bias, racial bias) to stress-test agent performance and integrating external tools like medical notebooks and search engines [99].
* *Knowledge Graph Integration:* Utilizing knowledge graphs constructed from clinical data (e.g., MIMIC-III) and retrieval-augmented generation to ensure patient responses are factually consistent and grounded in medical history [124].

Validation approaches:

* *Human and Expert Evaluation:* Validating simulation realism and clinical utility through blinded reviews by domain experts (e.g., clinicians, psychologists) [56, 99, 127], and user studies with medical trainees to assess educational effectiveness compared to human actors [124].

* *Quantitative Benchmarking:* Measuring agent performance against established medical standards, such as diagnostic accuracy on USMLE-style datasets (MedQA) [60, 99] and the ability to recover ground-truth user profiles (e.g., sleep goals, barriers) from conversation logs [127].

* *Comparison against Human Reference Data:* Analyzing the distribution of generated behaviors against reference populations [127].

* *Robustness and Stress Testing:* Evaluating system resilience by introducing cognitive and implicit biases to measure performance degradation [99] and testing stability across different personality profiles and input variations [124].

* *Psychometric and Patient-Centric Metrics:* Utilizing standardized psychological frameworks (e.g., CTRS, PANAS) to assess counseling quality [56], and employing novel metrics such as patient compliance, confidence in the doctor, and empathy ratings to evaluate the interactive quality of care [99, 124].

* *Real-World Alignment:* Verifying that skills acquired within the simulation (e.g., medical examination selection) transfer to improved performance on real-world benchmarks and align with human clinical decision-making patterns [60, 124].

## 6.5 Political Science, Public Policy, and Communication

Researchers in political science, public policy, and communication employ LLM-based agents to study opinion formation, voting behavior, and information propagation within populations. In these studies, agents represent voters, citizens, or social media users interacting with each other and with policy or platform rules. The goal is to understand large-scale effects such as polarization, echo chambers, policy support, or the spread of rumors and misinformation.

Core applications:

* *Simulating Public Opinion and Voter Behavior:* Multi-agent simulations model dynamics and shifts over time. For example, Zuo et al. [151] show how linked opinions on several topics evolve together and how echo chambers can emerge. Lin et al. [63] simulate online forums to observe group conformity, opinion shifts toward extremes, and the rise of smaller subgroups. Other work models persuasion between agents in repeated debates [17], and traces how alignment and group discussion can shape group-level biases [22]. At the electoral level, Yu et al. [123] simulate U.S. elections with many voter agents, while Kreutner et al. [53] study persona-driven voting in the European Parliament.

* *Modeling Policy Impacts on Synthetic Populations:* Several studies use LLM-based citizens to test the effects of public policies in a controlled setting. VacSim [44] models 100 agents with demographics, social ties, and vaccine attitudes to study how mandates or incentives affect vaccination uptake. Hao and Xie [42] simulate economic actors grouped by income and education to explore how tax policies impact different segments. Lazebnik and Shami [55] use LLM-guided taxpayer agents with distinct personalities and backgrounds to study how tax compliance patterns arise under different enforcement and service conditions.

* *Simulating Information and Misinformation Spread:* A third line of work focuses on social media and communication platforms. OASIS [121] models a large social network where agents post, follow, and react, enabling the study of
information diffusion and herd behavior. S³ [33] and its extension GA-S³ [137] simulate how content, opinions, and emotions propagate through a network, including group-level agents. MOSAIC [65] examines how agents like, share, or flag content, and how safety training changes their willingness to spread unverified information. RumorSphere [67] uses AI agents to study how rumors spread in large populations and tests which interventions, such as early and repeated correction by influential users, are most effective.

Techniques for realistic simulation:

*   **Persona and Demographic Grounding:** Many studies assign each agent a detailed persona that includes demographics (e.g., age, income, region), party or ideology, and sometimes values or traits. These personas can be hand-crafted, sampled from survey data, or learned from real-world statistics. This makes it possible to simulate different voter types, citizen groups, or media audiences and to analyze subgroup responses [53, 123].

*   **Networked Multi-agent Interaction:** Agents are often placed on explicit networks that encode social connections, such as followers or interlocutors. Opinion updates then depend on messages received from neighbors in the network, which supports the study of echo chambers, community formation, and cross-group contact [33, 63, 151].

*   **Policy and Environment Modeling:** Policy and platform rules are encoded as external conditions that shape what agents can do and what they observe. Examples include tax rates and enforcement levels [42, 55], vaccine mandates and incentives [44], or ranking and moderation rules on social media [65, 67, 121]. Modifying these settings makes it possible to run counterfactual scenarios.

*   **Individual and Group-level Agents:** Some frameworks include both individual agents and group agents. Group agents represent parties, factions, or communities and stand in for large sets of individuals. GA-S³ [137], for instance, uses group agents to scale up diffusion studies while still keeping a link to human-like behavior.

*   **Update Rules for Opinions and Actions:** Simulations combine LLM-generated messages with simple update rules that decide how agents change their beliefs and actions. These rules can draw on classic models of opinion dynamics (e.g., weighted averaging, threshold models) or on domain knowledge about policy response, and they may differ between studies [55, 63, 151].

Validation approaches:

*   **Comparison to Real Polls and Election Data:** Some work compares simulated outcomes to real survey or election results. This includes comparing vote shares, turnout, or support levels for policies across demographic groups, and checking whether changes over time follow observed trends [44, 53, 123].

*   **Matching Macro-level Patterns:** Another form of validation looks at aggregate patterns such as the degree of polarization, the clustering of opinions, or the speed and reach of information spread. Simulations are judged by whether they reproduce known features of real data, such as echo chambers, group splits, or typical diffusion curves [33, 63, 67, 121, 151].

*   **Stress Tests and Sensitivity Analysis:** Studies often vary model size, prompt design, network structure, or policy parameters to see how sensitive outcomes are to these choices [22, 63, 65]. Large shifts under small changes can indicate that the simulation is brittle or driven by artifacts of the LLM rather than by robust processes.

*   **Expert and Qualitative Review:** In some cases, domain experts (e.g., political scientists or policy analysts) review simulated discussions, opinion shifts, and policy responses. They judge whether argumentation, group dynamics, and outcomes are plausible and whether key mechanisms are present or missing [44, 67, 121].
<figure><img src="image_5.png" /><figcaption>Fig. 4. Overview of LLM-based human behavior simulation resources. Two main types: *User Simulation Toolkits* (left) for system evaluation, and *Social Simulation Frameworks* (right) for collective behavior and learning studies.</figcaption></figure>

# 7 Toolkits, Frameworks, and Resources for LLM-based Human Behavior Simulation

In this section, we review toolkits, frameworks, and reusable resources that support *human behavior simulation using LLMs*. We use the term *simulator* to refer to the overall system that generates human-like behavior, and the term *agent* to refer to an LLM-based component inside such a simulator that takes actions in an environment.

To ensure the relevance and utility of the resources reviewed, we applied the selection criteria outlined in Section 3.1. Specifically, we include resources that: (1) use LLMs as a core component for simulating human behavior, (2) are relevant to the fields of interactive intelligent systems or social and behavioral sciences, (3) provide a publicly accessible implementation or API enabling repeatable experiments, and (4) are sufficiently documented. We exclude purely conceptual frameworks without code, private tools, or pre-LLM simulators that have not been adapted to use LLMs.

In this section, we organize the toolkits given the two categories of human behavior simulation (Section 2), i.e., *user simulation* and *social simulation*, a high-level overview is presented in Figure 4.

## 7.1 Toolkits for User Simulation

A core use of LLM-based simulation is to study interactive systems, such as search engines, recommender systems, and conversational assistants. In this setting, agents assume the role of users who interact with a system over one or more sessions. Toolkits differ in the granularity of user actions, the representation of user state, and the ease of integrating new systems.

### 7.1.1 *Search and Recommendation.*
For search engines, the primary aim is often to simulate full interaction loops rather than single queries. **USimAgent** [131] and **SimIIR 3** [9] are representative examples. USimAgent uses an LLM in a reasoning-action loop to decide which query to issue, how to examine the results page, which document to click, and when to stop or reformulate. SimIIR 3 extends the long-standing SimIIR toolkit with LLM-based agents, allowing search behavior to be simulated under explicit control of tasks, stopping rules, and user states. This highlights the shift from query generation to a richer model of user decision making and information needs.

In the domain of recommender systems, many toolkits emphasize persona-driven simulation and long-term behavior. **Lusifer** [26] uses prompt templates to generate user feedback on recommended items, facilitating the offline evaluation of online recommenders. Other work [138] utilizes LLM-powered agents to provide engagement signals—such as clicks and ratings—to train and test recommendation policies. **RecAgent** [111] offers a more complex example. It simulates users who not only interact with recommendations but also chat, post, and form a social history. RecAgent includes an
explicit memory module to maintain preference consistency across time, supporting more dynamic and context-aware
behavior.

*Mapping to the framework.* These toolkits are situated within the interactive intelligent systems context (Section 5). They mainly model: (i) *decision making* (queries, clicks, ratings, next actions), (ii) *information needs*, (iii) *knowledge level* and preferences, often defined via personas, and (iv) simple *internal states* such as satisfaction or patience. The temporal scope usually covers full tasks or sessions; actions are modeled at a fine level (queries, clicks, page views). State is typically dynamic within a session, with some toolkits also tracking longer-term profiles. Persona complexity ranges from simple types (e.g., novice vs. expert) to richer profiles with traits and interests.

### 7.1.2 Conversational Assistants.
In conversational assistants, the main goal is to simulate multi-turn dialogues with a system. User simulators typically have a specific goal, react to system replies, and are parametrized with a persona.

Early work demonstrated that general-purpose LLMs can serve as user simulators through prompt engineering alone. **ConvLab-3** [148] uses prompts to drive an LLM that acts as a user in task-oriented dialogues. Similarly, the framework by Terragni et al. [107] leverages in-context learning with few examples to steer user behavior. In conversational recommendation, **iEvaLM** [114] and **UserSimCRS v2** [12] also rely on in-context learning, but in a zero-shot manner, to guide the LLM-based users in disclosing information related to their goals, reacting to recommendations, and stopping the conversation.

Prompt-only methods are flexible but can drift from the intended task or goal. To mitigate this drift, some toolkits rely on fine-tuning. Sekulic et al. [103] present a domain-aware simulator for scientific information access. By fine-tuning the LLM on in-domain conversations, the resulting behavior is better aligned with domain goals and user intents.

Other toolkits target specific conversational tasks. For instance, **GenIRSim** [50] simulates interactive debate and argumentation. Here, the user simulator not only asks questions but also advances arguments and counterarguments, requiring more explicit reasoning over content.

*Mapping to the framework.* These toolkits also fall under the interactive intelligent systems context. They center on: (i) *decision making* in terms of user intents and dialogue acts, (ii) *information needs* bound to conversational goals, and (iii) occasionally simple *internal states* such as cooperativeness or politeness. Temporal scope covers full conversations. Actions are low-level (utterances at each turn). State is dynamic across turns, and some toolkits maintain explicit goals and user knowledge. Personas range from minimal goal descriptions to richer profiles with social traits and styles.

## 7.2 Toolkits for Social Simulations

The second major category of resources supports the simulation of social processes. Unlike system evaluation toolkits, these frameworks are designed to model agents interacting with each other or with a complex environment to study emergent phenomena, learning processes, or social dynamics. This category encompasses resources for social science, political science, and educational environments.

### 7.2.1 Sandbox Worlds and Agent Societies.
Sandbox worlds place LLM-based agents in a shared environment with simple rules and open-ended interaction. The **Smallville** system [87] is a widely cited example. It simulates a small town with 25 agents. Each agent has a profile, memory, and planning module, and can communicate with others and execute actions in the world. The study reports emergent behaviors, such as spontaneous parties and information spread, that were not manually scripted. Subsequent work builds on this foundation. **GenAgents** [88] grounds agent personas in real interview data, which links the simulated agents more closely to observed human profiles. **AgentVerse** [19]
offers a general multi-agent framework that can host social simulations, such as a virtual classroom. **Sotopia** [145] provides an open-ended environment for social learning and the evaluation of agent social skills. **TinyTroupe** [94] focuses on small groups of agents with rich memory and supports the detailed study of interactions in such groups. Scaling up, **AgentSociety** [120] aims to simulate tens of thousands of agents in realistic urban, social, and economic environments. It targets large-scale social processes such as mobility, consumption, and communication. From an infrastructure perspective, **AgentTorch** [21] provides a differentiable agent-based modeling framework that runs on GPUs and can simulate cities with millions of agents while still allowing LLM-based “archetypes” to drive agent behavior. **SocioVerse** [135] takes a complementary approach as a world model for social simulation powered by LLM agents and a pool of 10 million real-world users; it offers modular components for aligning environment, users, scenarios, and behaviors with real data across domains such as politics, news, and economics.

*Mapping to the framework.* These frameworks fall under the overall social simulation context, specifically focusing on emergent social dynamics. They primarily model: (i) *decision making* regarding high-level plans and autonomous social interactions, (ii) complex *internal states* including evolving memory, beliefs, and interpersonal relationships, and (iii) *reasoning* required for long-term planning and social navigation. The temporal scope is typically long-term and continuous to allow phenomena to emerge. The persona complexity is high, often involving detailed backstories and distinct social roles.

### 7.2.2 Platform-Specific Social Environments.
Some toolkits focus on social media or communication platforms. The goal is to simulate how posts, opinions, and emotions propagate through a network. **OASIS** [121] is a large-scale social media simulation, designed to mirror platforms such as Twitter and Reddit. Agents can post, follow, and comment, and a recommendation module suggests content. This setup supports experiments on diffusion, herd behavior, and platform policies. The $S^3$ (**Social-network Simulation System**) [33] models agents with emotions and attitudes, which change as they interact. Content and affect spread through the network over time. $GA-S^3$ [137] extends $S^3$ with group agents, which represent sets of individuals and facilitate the simulation of large populations.

*Mapping to the framework.* These toolkits belong to the communication context. They primarily model: (i) *decision making* (what to post, share, or support), (ii) *internal states* (beliefs, emotions, social ties), (iii) *reasoning* about other agents and new information, and (iv) *knowledge level* and exposure history. The temporal scope is typically long-term with many steps. Actions are at a fine level (posts, comments, likes, follows). State is highly dynamic, as agents update beliefs, emotions, and relations over time. Personas tend to be complex, with roles, traits, and positions in a network.

### 7.2.3 Toolkits for Educational Environments.
Resources for educational settings also fall under social simulation, as they model the complex behaviors of learning and teaching in a classroom environment. In such environments, agents act as students, teachers, or peers. **SimClass** [139] simulates a classroom with teacher and student agents. The teacher responds to student questions and attempts to adapt to their knowledge level. SimClass also supports human-in-the-loop interaction, where real students can join the simulated class. **EduVerse** [72] offers a similar, user-defined multi-agent classroom. Agents can take roles such as teacher, teaching assistant, or student. States evolve throughout a session, and human users can interact with the simulated class. **MathVC** [126] focuses on mathematics learning. It provides a virtual classroom where a coach and LLM-based student characters interact around math problems, providing a structured environment for practice and study.
*Mapping to the framework.* These frameworks cover the *education* context. They primarily model: (i) *internal states* (beliefs, emotions, social ties, confusion), (ii) *knowledge level* (what is known, unknown, or misunderstood), (iii) *decision making* (posting, sharing, learning strategies), and (iv) *reasoning* about others and new information. The temporal scope is typically long-term. Actions are fine-grained (posts, comments, dialogue turns). State is highly dynamic, as agents update beliefs and relations over time. Personas include student profiles and teacher styles, with moderate to high complexity.

7.3 Outlook and Recommendations

The field of LLM-based user simulation is undergoing rapid expansion. The toolkits and resources we have cataloged provide a strong foundation. However, several key challenges must be addressed for the field to mature from qualitative demonstrations to a rigorous, quantitative science. Based on our review of resources, we identify emerging architectural standards alongside three primary challenges, and offer corresponding recommendations for the community.

*Common Design Patterns.* Current frameworks already exhibit common architectural patterns. First, most toolkits represent agents as modular entities. Typical modules include a profile or persona, a memory of past events, and a policy that maps current state and context to actions. LLMs are often integrated within several of these modules to update memory, infer intent, or generate actions. Second, many frameworks include an explicit environment layer. This layer conveys observations to agents and applies their actions, whether the environment is a simulated search engine, a social network, or a virtual classroom. Third, there is often an orchestration component that manages time, logs events, and controls the sequence of interactions in multi-agent settings. These established patterns provide a solid baseline for future development.

*Validation and Grounding.* A central challenge is demonstrating that a simulator accurately reflects real-world human behavior. Many current studies rely on the subjective “human likeness” of agents, which is insufficient for rigorous science. We identify two critical areas for improvement: grounding and validation. Grounding involves ensuring that agent profiles and initial conditions are representative of real populations based on empirical data. Validation involves comparing the simulation outcomes against observed human behaviors and trends. Currently, systematic validation against large-scale human datasets is rare. We recommend that future work prioritize the development of robust validation protocols with built-in quantitative analysis tools rather than just focusing on novel demonstrations. GenAgents [88] and AgentSociety [120] point toward this direction, but systematic validation remains limited.

*Long-term Dynamics and Memory.* Another major challenge lies in modeling statefulness and memory. A simulator produces meaningful long-term dynamics only if its agents can change over time. If an agent's internal state is fixed or its memory is limited to a short context window, it cannot truly learn or adapt. Consequently, behavior often becomes a series of disconnected, repetitive actions rather than a coherent, evolving process. Some toolkits, such as RecAgent [111] and TinyTroupe [94], add richer memory modules and retrieval schemes to address this. A key recommendation for future research is to focus on creating psychologically plausible mechanisms for retrieving and synthesizing memories to influence future decisions in a stable way.

*Fragmentation and Comparability.* Finally, the current ecosystem of toolkits is fragmented. Most frameworks define their own environments, roles, and metrics. This prevents the comparison of results across studies. An “emergent behavior” observed in one toolkit cannot easily be reproduced in another. Standardization is critical to build a cumulative body of scientific knowledge. We recommend the development of common benchmarks, shared tasks, and open

Manuscript submitted to ACM
evaluation protocols to facilitate research and ensure comparability. Platforms such as Sotopia [145], which are designed
as shared testbeds for social behavior, are a step toward such standardization.

In summary, existing toolkits already allow researchers to build rich simulation studies in many domains. The most promising path forward is to prioritize hybrid architectures where LLMs provide high-level reasoning and are coupled with more specialized models for memory or specific domain rules. Furthermore, addressing the infrastructure required for scale is a prerequisite for studying complex societal dynamics. By focusing on careful grounding, stronger validation, and shared resources, LLM-based human simulations can serve as reliable tools across fields.

8 Open Research Directions

Informed by our review, we now discuss open directions for future research on LLM-based human behavior simulation.

*Grounding Simulation.* LLMs are trained primarily for text processing and generation. While human behavior can be described textually as a sequence of actions, the process of generating human behavior is distinct from standard text generation. The proportion of behavioral data in the training corpus of LLMs is likely limited. Furthermore, human behavior is often influenced by internal states and contextual factors, leading to more long-tail behavioral patterns [36]. To ensure that generated behaviors are realistic (spanning common, long-tail, and irrational behaviors) and aligned with specific conditions, the community has been exploring grounding mechanisms (e.g., in-context learning and goal alignment verification). Although some progress has been made, empirical studies have reported deviations from expected behaviors in certain contexts [85, 118, 122]. This highlights opportunities for further research in this direction, for example, by investigating new grounding mechanisms and determining when to apply them in the simulation process (e.g., prior or post to the generation of the next interaction).

*Personalization of Simulation.* We observe a trend towards more personalized simulation, that is, moving away from simulating the “average user.” In the current literature, a persona or profile is used to parameterize the simulator to represent user populations or individuals with different behaviors. However, at this point, there are no standardized ways of doing this and several open questions remain, such as: Which attributes should be used for personalization? How can they be modeled them effectively? How can we assess their influence on the generated behavior? To what extent can we personalize simulation given that all simulated users rely on the same underlying internal knowledge? Datasets including rich user profiles, behavioral data (e.g., think-aloud traces and post-interaction interviews), and contextual factors (e.g., time of day, location, social context) would be beneficial to investigate these questions. However, collecting such datasets raises ethical and privacy concerns that need to be addressed.

*Validation Methods.* Different categories of methods have been used to validate LLM-based human behavior simulation, as discussed in Section 4.5. However, we note that the research community as a whole is still exploring best practices for validation. The methods and metrics underlying the different categories of validation vary within and across application contexts. This hinders the comparison of different simulators over time. This necessitates future research to establish commonly accepted validation protocols and metrics for LLM-based human behavior simulation, along with best practices and guidelines to support their adoption. In particular, the development of standardized methods to assess the realism of simulated behavior, such as CoMPOsT [20], remains an under-studied area.

*Specialized Language Models.* As previously mentioned, general-purpose LLMs were not specifically designed to simulate human behavior, raising questions regarding their suitability for this task. This has led to the development of
different types of specialized language models. For example, Gong et al. [36] propose a new pretraining paradigm with
which they train BehaveGPT, a foundational behavioral LLM specialized for next behavior prediction. Moreover, in
the context of conversational interactions, the latest versions of general-purpose LLMs are often optimized to act as
helpful assistants. Naous et al. [83] argue that new language models specifically designed for user simulation are needed
to build more realistic simulators. They propose User Language Models and empirically show that they outperform
general-purpose LLMs on user simulation in a conversational context. These works suggest that tailoring training
towards the user perspective are a promising direction for future research.

*Architectures.* Across diverse application contexts, different architectures have been proposed to build LLM-based human behavior simulators. However, a comparative analysis of these architectures is still limited, making it difficult to identify the strengths and weaknesses of each architecture and their generalizability across contexts. Another interesting comparison would be between simulators relying on different types of LLMs, such as general-purpose LLMs, fine-tuned general-purpose LLMs, user language models, and foundational behavioral LLMs. Performing such comparisons would provide insights into the trade-offs between flexibility, performance, and resource requirements of different architectures across application contexts. These insights could guide the design of future LLM-based human behavior simulators, that offer better coverage in terms modeled behavioral aspects, simulation scope, and complexity.

*Interpretability.* Explaining LLM decisions and outputs is an active research area [141]. This is important in the context of human behavior simulation, as it would help to understand the rationale behind the simulated behaviors. However, solutions for auditing or probing LLM-based human behavior simulators are still limited. Future research in this direction could contribute to new methods and tools to interpret and debug LLM-based human behavior simulators, enhancing their transparency and validity.

*Infrastructure.* While simulation of human behavior has been studied in multiple communities and for a wide range of different purposes, our review revealed that there are commonalities shared in the simulation approaches. It is thus beneficial to create some kind of shared infrastructure for researchers from different communities to develop and improve simulation techniques in the same unified conceptual framework both to avoid duplicated effort and to enable technology transfer across domains (e.g., making the most advanced LLM-based simulation techniques developed in the AI community immediately available for social science researchers to use in their social experiments).

9 Conclusion

Recently, interest in LLM-based human behavior simulation has grown rapidly across multiple research fields. In this survey, we provided a comprehensive overview of existing work and resources on this topic spanning the fields of interactive intelligent systems and social and behavioral sciences. This overview is organized according to the three dimensions of our proposed conceptual framework to characterize human behavior simulation: (1) application context, (2) aspects of human behavior modeled, and (3) simulation scope and complexity.

From this survey, we derive several observations. We identified distinct application contexts across the fields reviewed; however, the underlying purposes of simulation often overlap. For example, different communities use simulation to generate synthetic data for training or analysis, whether in the context of education or recommender systems. In terms of the aspects of human behavior modeled, we noted varying degrees of coverage between application contexts. A similar observation applies to simulation scope and complexity. These observations allow us to map existing work within an interdisciplinary and multi-dimensional landscape. This mapping serves multiple purposes. First, it provides
pointers to relevant work and resources across different fields for anyone interested in applying LLM-based human
behavior simulation for specific use cases. Second, it helps to identify gaps, challenges, and opportunities for the
development of the next generation of LLM-based human behavior simulation approaches.

This survey reinforces our belief that interdisciplinary collaboration is essential to advancing research on this topic.
Indeed, it appears that different communities have developed complementary expertise and insights, specifically in
modeling particular aspects of human behavior, personalization, and validation methods. By bringing together these
different perspectives, we believe that more holistic and realistic simulation approaches can be developed, along with
more generalizable resources to support research in this area.

Limitations

We acknowledge that this survey has some limitations. First, LLM-based human behavior simulation is evolving at a fast pace, with new papers and resources being released frequently. Consequently, some recent work might not have been included in this survey. Moreover, some relevant work might not have been captured by our search queries and selection criteria. Second, this survey focuses on the fields of interactive intelligent systems, psychology, and social and behavioral sciences. There may be relevant work in other fields that we did not cover. However, we note that the proposed framework allows researchers to easily situate new papers within the context of existing literature.

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