Large Language Models Assume People are More Rational than We Really are

## Abstract

Abstract In order for AI systems to communicate effectively with people, they must understand how we make decisions.
However, people’s decisions are not always rational, so the implicit internal models of human decision-making in Large Language Models (LLMs) must account for this.
Previous empirical evidence seems to suggest that these implicit models are accurate — LLMs offer believable proxies of human behavior, acting how we expect humans would in everyday interactions.
However, by comparing LLM behavior and predictions to a large dataset of human decisions, we find that this is actually not the case: when both simulating and predicting people’s choices, a suite of cutting-edge LLMs (GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more rational than we really are. Specifically, these models deviate from human behavior and align more closely with a classic model of rational choice — expected value theory.
Interestingly, people also tend to assume that other people are rational when interpreting their behavior. As a consequence, when we compare the inferences that LLMs and people draw from the decisions of others using another psychological dataset, we find that these inferences are highly correlated.
Thus, the implicit decision-making models of LLMs appear to be aligned with the human expectation that other people will act rationally, rather than with how people actually act.

## 1 Introduction

Figure: Figure 1: Two tasks we use to assess the implicit assumptions that LLMs make about human decision-making. (A) Predicting choices between gambles. Each gamble is described by the probabilities and values of different outcomes, and the goal is to predict which gamble people will choose. (B) Inferring preferences from choices. Here, a person chooses one of many sets of objects and the goal is to infer their preferences based on that choice.
Refer to caption: extracted/5764178/figures/main_fig.png

Every day, our actions are based on the countless decisions that we make reflecting our internal goals and beliefs about the world. Through our interactions with others, we are able to effortlessly predict how other people would act from their goals and beliefs, and infer others’ goals and beliefs when observing their choices. These abilities — termed forward- and inverse-modeling in cognitive science (Ho and Griffiths, ) — are characteristic of the implicit decision-making models that we form of others, and are crucial to interpersonal communication and learning (Baker et al., ; Lucas et al., ; Jara-Ettinger et al., ).
However, while these abilities are inherent in people, the accuracy and consistency of decision-making models in Large Language Models (LLMs) is unknown.
As LLMs become widely used as the basis for building AI agents that interact with or simulate people, it is important to ask what decision-making models LLMs implicitly use: the ability to predict and interpret people’s behavior is a precursor to identifying effective ways to provide assistance, simulating the helpfulness or harmlessness of a response, and learning individuals’ values and preferences, all of which are principal to the development of safe and beneficial AI systems.

Though LLMs have become increasingly capable of conducting reasoning and conversing with humans, there is no guarantee that their implicit representations of humans align with how we actually behave. Methods such as Proximal Policy Optimization (Schulman et al., ) and Direct Preference Optimization (Rafailov et al., ) can be used to tune models on explicitly declared human preferences (Ouyang et al., ), but training data such as blog posts, news articles, and books go through rounds of editing that remove logical fallacies and other mistakes, leading to more “perfect” content being used for training (Cui et al., ; Zhou et al., ). While this improves the quality of generation, it may also lead LLMs to develop mistaken impressions of how humans actually behave.
Furthermore, many existing measures comparing LLM simulations to human behavior use human perception to measure similarity (Park et al., ; Jones and Bergen, ; Jakesch et al., ; Hämäläinen et al., ), but existing psychology literature has shown that people’s own perceptions of others may be more rational than they actually are (Jern et al., ). Thus, an AI system that appears human-like to the naked eye may not actually correspond to human behavior.

This phenomenon is not without precedent — early economists did not view the assumption of human rationality as problematic (Smith, ; Mill, ) and built sophisticated models and policies (Downs, ; Coleman, ; Schelling, ; Dunleavy, , *inter alia*), until psychologists showed just how systematic and widespread its failures were in accounting for human behavior (Tversky and Kahneman, ; Kahneman and Tversky, ).
As LLM-powered systems become more widely-used, misaligned representations of humans — which can lead to mismatched beliefs and failure to follow instructions (Milli and Dragan, ) — may pose a greater toll on various downstream applications. But how can we study these implicit decision-making models without relying on human judgments?

To explore the implicit decision-making models of LLMs, we leverage two existing experimental paradigms from psychology — a risky choice task where humans choose between gambles (Bourgin et al., , Figure 1A), and an inference task where people reason about others’ subjective utilities after observing their decisions (Jern et al., , Figure 1B). In the former, we compare choices that participants made with LLM simulations and predictions of those choices (forward modeling; Ho and Griffiths, ), while in the latter we compare the inferences drawn between models and humans over the same observations (inverse modeling). These two paradigms are connected by the same foundational theoretical models of human decision-making, under which people develop mental models of others’ goals, utilities, and decision spaces, and use these to 1) construct predictions about how others will behave given their beliefs, and 2) infer what people believe based on their decisions (Baker et al., ; Lucas et al., ; Jara-Ettinger et al., ; Ho and Griffiths, ).

Through these experiments, we find that LLMs model people as highly rational decision makers. For forward modeling, we discover that when prompting with chain-of-thought (CoT; Wei et al., ), LLMs consistently predict that humans act more rationally than they do; today’s most capable LLMs produced correlations greater than 0.94 with the rational model of choosing based on the maximum expected value, but humans only have a correlation of 0.48.
Additionally, asking LLMs to simulate the decision with CoT also yields highly rational outcomes that are only moderately correlated with human behavior.
In both cases, zero-shot prompting generates noisy outcomes that are only moderately correlated with either rational models or humans.

In the inverse modeling paradigm, we find that the inferences that LLMs make from peoples’ choices are also consistent with the assumption that humans are rational actors. Across two contexts, LLMs’ inferences positively correlate with predictions from rational models, with these correlations steadily increasing with model capabilities and from zero-shot to CoT (0.20 with Llama-3-8B zero-shot; 0.95 with GPT-4o CoT).
However, the psychology literature suggests that despite deviating from rationality in their own choices, people’s inferences from the behavior of others are consistent with assuming humans are rational agents (Baker et al., ; Lucas et al., ; Jern et al., ; Jara-Ettinger et al., ). Consequently, we also find that the inferences made by LLMs from others’ choices are very highly correlated with the same inferences made by people. Thus, while LLMs are not accurate at simulating or predicting human behavior, LLMs’ assumption that people are more rational than we really are aligns with the assumption that people make when interpreting one another’s behavior.

The remainder of the paper is organized as follows. In Section 2, we introduce related work across LLMs, alignment, and models of decision-making in cognitive science. Section 3 and Section 4 describe the forward and inverse modeling experiments and results, detailing our analyses and the correlations between LLMs, humans, and existing rational theories. Finally, in Section 5, we discuss potential insights related to our work, including implications towards the feasibility of simulating humans using LLMs, how RLHF can bias models towards aligning with what humans do instead of what humans expect others to do (and vice versa), and the potential of using cognitive science to inspire different forms of alignment research.

## 2 Related work

### 2.1 Aligning large language models with humans

Large Language Models are typically aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF) (Bai et al., ; Ouyang et al., ). Training with human preference data has been shown to enhance reasoning in LLMs  (Havrilla et al., ).
The impressive capacities of the resulting models (e.g., Bubeck et al., ) have sparked interest across various fields in using them to model (e.g., Binz and Schulz, ; Macmillan-Scott and Musolesi, ) and simulate (e.g., Park et al., ; Argyle et al., ; Liu et al., ) human behavior.
Even though aligned LLMs can effectively approximate average human judgments (Rathje et al., ), these models still exhibit biases and output hallucinations (Jiang et al., ; Bai et al., ; Anwar et al., ). Moreover, LLMs may not be adept at capturing trade-offs in human behavior (Liu et al., ; Coletta et al., ) or situations with information asymmetry (Zhou et al., ).

Methods from cognitive science are increasingly being used to study LLMs (Hardy et al., ; Coda-Forno et al., ; Binz and Schulz, ). A particularly contentious debate is whether LLMs exhibit Theory of Mind, the ability to model others’ mental states which may be different than their own (Premack and Woodruff, ). Several studies have shown evidence for (Kosinski, ) and against (Sap et al., ; Ullman, ) this claim, and have explored the ability of LLMs to accurately emulate various personas that should result in changes in their decision-making behavior (Salewski et al., ). Our analysis provides a quantitative approach to engaging with this debate, as inverse decision-making is a specific form of Theory of Mind.

### 2.2 Forward and inverse models of human decision-making

One of the most basic and extensively-studied problems in decision-making is the risky choice task (Kahneman and Tversky, ; Edwards, ; Peterson et al., ; Bourgin et al., ), where people choose among gambles with different outcome probabilities and payoffs. The rational action is to choose the gamble with the highest expected value, calculated by summing the product of the probabilies and the values of the outcomes. Humans have been described as deviating from rationality in a fourfold pattern: risk seeking for small probability gains, risk averse for small probability losses, risk averse for moderate and large probability gains, and risk seeking for moderate and large probability losses (Tversky and Kahneman, ; Kahneman and Tversky, ).
Peterson et al. () collected the choices13k dataset, a large dataset of over 13,000 human choices between different gambles, and showed that people’s decisions in this setting could be captured by relatively simple machine learning models. Binz and Schulz () used these data to fine-tune an LLM, achieving similar performance. Chen et al. () built a risky choice dataset and found that GPT-3.5 makes economically rational decisions, which we replicate in task 3 of our forward modeling experiments on the choices13k dataset across various LLMs.

People are able to infer an agent’s beliefs, desires, and percepts from their actions (Baker et al., ; Lucas et al., ; Jara-Ettinger et al., ; Ho and Griffiths, ). These inferences are typically modeled by assuming that people employ a forward model of decision-making — typically a noisy rational model — and use Bayes’ rule to invert that model (for more details, see Section 4).
The study that we focus on, by Jern et al. (),
was intended to directly test this assumption.
Similar approaches have been used to improve human-AI interaction (e.g., Dragan et al., ; Sadigh et al., ). Recent work has extended these models using LLMs to make inferences from utterances as well as actions (Zhi-Xuan et al., ; Ying et al., ).

## 3 Forward modeling: Predicting which gamble people will choose

Our forward modeling experiments used a traditional risky choice task from psychology, where participants choose between two options that differ in the probability of returning rewards.

#### Tasks.

To comprehensively understand how LLMs empirically capture human intent and align with actual human decisions, we designed three forward modeling paradigms. First, we asked LLMs to predict the decisions that a human participant would make. Second, we asked LLMs to predict the proportion of participants that would select each option. Third, we instructed LLMs to simulate participants by making the decisions themselves. For each task we implemented both zero-shot and chain-of-thought (CoT; Wei et al., ) prompting. Our code is available at [https://github.com/theryanl/LLM-rationality](https://github.com/theryanl/LLM-rationality).

#### Human data.

Human choice data came from the choices13k dataset (Peterson et al., ; Bourgin et al., ), a comprehensive collection of 13,006 risky choice problems. Each choice problem consists of two options, where each option is a set of rewards with corresponding probabilities (e.g., would you rather win $5 with probability 1 or take a 0.5 probability of winning $7?). The dataset includes the proportion of people who selected each option for each problem. Our analyses used a subset of 9,831 of these problems that were not “ambiguous” (where probabilities were not shown) or lacked feedback. We used the data to evaluate the alignment of LLMs in each of the three paradigms with actual human choices.

#### Language models.

We evaluated the following general-purpose models, including both open-sourced and closed-sourced models: Llama-3-8B, Llama-3-70B, Claude 3 Opus, GPT-4-Turbo (0125-preview), and GPT-4o. We implemented experiments on the full choices13k dataset for zero-shot and chain-of-thought (CoT) prompting across the three different tasks mentioned above for all models besides Claude. For Claude 3 Opus, we only evaluated the first task — predicting what a human participant’s choice might be — due to cost limitations.

For the experiments predicting and simulating individual human decisions (tasks 1 & 3) and models Llama-3-8B, Llama-3-70B, and GPT-4-Turbo, we conducted zero-shot experiments with $n$ completions, where $n$ is the number of participants that made the same decision in the choices13k dataset (ranging from 15 to 33).
Each completion involved predicting or simulating a single human participant’s decision, with temperature set to 0.7 to maintain sample diversity.
For the corresponding CoT experiments, we observed that responses were completely deterministic at temperature 0.7 on a random subset of 1000 decisions, and thus prompted for 1 completion with temperature 0.0 across the entire dataset.
For the same reason, we ran the experiments predicting the proportion of humans who would select an option (task 2) for 1 completion for both zero-shot prompting and CoT prompting with temperature 0.0.

We adapted a prompt template previously used by Binz and Schulz (, see Appendix A). For each choice problem, we first introduced the decision context of the choices13k dataset (i.e., the idea of choosing between gambling options) before providing each option’s probabilities and associated rewards in dollars. Finally, we asked what the participant(s) would choose (for predicting decisions) or what “you” would choose (when acting as a human participant). To minimize any positional biases (Wang et al., ), we shuffled the order of the options presented in the prompt. Example prompts are in Appendix A.

### 3.1 Results

To evaluate whether LLMs are able to predict or simulate human risky choice decisions, we computed correlations and mean-squared errors (MSE) between LLMs’ responses and human decisions. We report both Pearson and Spearman correlation, but find that they are extremely similar and thus discuss them interchangeably. Additionally, we compared LLM responses to a classic model of rational choice: choosing the option with the highest expected value. In this case Pearson and Spearman correlation are identical because maximizing expected value results in a binary response.
Due to space considerations we focus here on the first of our three tasks — predicting individual choices — because the results were extremely similar across the three tasks. Results from predicting the proportion of human choices and acting as a human appear in Appendix B.

#### LLMs using zero-shot prompts are poor predictors of human choices.

We found that LLMs with zero-shot prompting are not well-aligned with human decisions. Table 1 shows the model predictions of human behavior against actual human choices, with GPT-4-Turbo performing best with a correlation of 0.60 and an MSE of 0.13.
These models are also not well-aligned with rational decision-making. Table 2 shows that the human correlation with the maximum expected value is 0.48, while even GPT-4-Turbo and GPT-4o only achieve correlations of 0.41 and 0.28, with MSEs of 0.27 and 0.36 respectively.(^1^11In the zero-shot case, we ran a single completion for GPT-4o but multiple for GPT-4-Turbo, which likely resulted in GPT-4o being less correlated with human choices due to variance in sampling.) In an effort to determine exactly what LLMs are doing, we fit a wider range of behavioral models to the responses of GPT-4-Turbo. This analysis suggests that the model does produce responses consistent with maximum expected value on a subset of choice problems, but also completely neglects probability information a significant proportion of the time (see Appendix D).

#### LLMs using CoT assume people are more rational than they are.

We found all LLMs with CoT prompting assume people act rationally, which is not aligned with actual human decisions. As shown in Tables 1 and 2, even Llama3-8B achieves a correlation with maximum expected value of 0.57, while humans only achieve a correlation of 0.48. This correlation rises as model capabilities improve. Llama3-8B obtains a correlation of 0.57 with an MSE of 0.22; Llama3-70B obtains a correlation of 0.80 with an MSE of 0.1; Claude 3 Opus obtains a correlation of 0.76 with an MSE of 0.12, while GPT-4-Turbo (which best predicted people) and GPT-4o obtain correlations of 0.93 and 0.94 with MSEs of 0.03 and 0.02. The same patterns held in the tasks asking for aggregate behavior or for LLMs to act as humans, although the aggregate behavior task had slightly reduced correlations with expected value (see Appendix B for details).

**Table 1: Correlation between LLM predictions of human choices and actual human choices.**
|  |  | Llama3-8B | Llama3-70B | Claude3 Opus | GPT-4-Turbo | GPT-4o |
| --- | --- | --- | --- | --- | --- | --- |
| Zero-shot | Spearman correlation | 0.3797 | 0.5300 | / | 0.6048 | 0.4756 |
| Pearson correlation | 0.3830 | 0.5270 | / | 0.5824 | 0.4718 |  |
| MSE | 0.1283 | 0.1142 | / | 0.1369 | 0.1987 |  |
| CoT | Spearman correlation | 0.4625 | 0.6156 | 0.5755 | 0.6393 | 0.6113 |
| Pearson correlation | 0.4611 | 0.6112 | 0.5750 | 0.6326 | 0.6164 |  |
| MSE | 0.1966 | 0.1633 | 0.1713 | 0.1595 | 0.1638 |  |

**Table 2: Correlation between LLM predictions of human choices and the maximum expected value.**
|  |  | Llama3-8B | Llama3-70B | Claude3 Opus | GPT-4-Turbo | GPT-4o | Humans |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Zero-shot | Spearman correlation | 0.1811 | 0.3378 | / | 0.4106 | 0.2843 | 0.4835 |
| Pearson correlation | 0.1811 | 0.3378 | / | 0.4106 | 0.2843 | 0.4835 |  |
| MSE | 0.3145 | 0.3378 | / | 0.2686 | 0.3579 | 0.2580 |  |
| CoT | Spearman correlation | 0.5665 | 0.7957 | 0.7566 | 0.9322 | 0.9444 | 0.4835 |
| Pearson correlation | 0.5665 | 0.7957 | 0.7566 | 0.9322 | 0.9444 | 0.4835 |  |
| MSE | 0.2181 | 0.1031 | 0.1228 | 0.0340 | 0.0278 | 0.2580 |  |

Although all the LLMs we investigated claim to be aligned with human preferences during the training, our empirical results suggest that aligning with “perfect” human data leads LLMs to assume humans act more rationally than they actually do, particularly when chain-of-thought prompting is used. In these settings, LLMs correlated more highly with maximizing expected value than with human choices, providing evidence of a gap between the implicit model of human decision-making assumed by LLMs and actual human behavior.

## 4 Inverse modeling: Inferring people’s preferences from their choices

While forward modeling predicts a person’s decision using established utilities, inverse modeling uses the same underlying decision-making models to infer a person’s utilities based on their made decisions, providing a complementary setting to evaluate the assumptions of LLMs. We adapt a task from a psychology experiment conducted by Jern et al. ().

#### Task.

In the experiment, human participants were asked to place 47 observed decisions of others into a rank ordering indicating how much each decision suggested that the decision-maker prefers a target item. We used this paradigm rather than one that focuses on measuring the inferred preferences for each individual decision (e.g., Lucas et al., ), because such rankings provide more information about the implicit model assumed by the ranker. Through these rankings, Jern et al. () found that humans ascribe almost perfectly rational decision-making models to the people they observe.

We found that GPT-4 Turbo could not provide a valid output ranking 47 choices at once. Instead, we obtained rankings by measuring pairwise comparisons between $47\choose 2$ pairs of decisions. Pairwise outputs were limited to {stronger, weaker, tie}, and were aggregated across decisions to form a ranking. Ties were discouraged in the prompt to capture small differences between decisions.

#### Dataset.

Each decision consisted of up to five unique items including the target item. Decisions were made between up to five distinct options. Items could be featured in multiple options within a decision, but never more than once in the same option. The target item was always in at least one option, and was always part of the chosen option.
Observers did not know anything about the values of individual items — instead, relative utilities were inferred after observing the decision maker’s choice.
Thus, in this paradigm, the remaining four items are equivalently exchangable; choosing {target item $X$} over {item $A$, item $B$} should yield same same inferred level of preference as choosing {target item $X$} over {item $C$, item $D$}. The 47 decisions were structurally unique, yielding coverage over all major decision types within this space. A full list of decisions is in Appendix G.

Decisions were instantiated within two contexts: one where all items are assumed to have a positive value (candies), and one with negative values (electric shocks).
These meaningfully change the inferences of a observed decision; choosing {candy $A$} over {candy $B$, candy $C$} indicates a strong preference for $A$, but choosing {shock $A$} over {shock $B$, shock $C$} does not.

#### Rational models.

Inverse decision-making models developed by psychologists to explain how people infer the preferences of others typically first specify a forward model and then infer preferences by applying Bayesian inference (Baker et al., ; Lucas et al., ; Jara-Ettinger et al., ; Ho and Griffiths, ). The forward model is normally a “noisy” version of a rational model, where options with greater utility are selected with higher probability. For example, given the utilities a decision-maker assigns to each item $\mathbf{u}$ and the attributes (items) within each of the $n$ options $\mathbf{A}=\{a_{1},\dots,a_{n}\}$, a standard model based on Luce () assumes that the probability of choosing option $o_{j}$ in choice $c$ follows

$$ $p(c=o_{j}|\mathbf{u},\mathbf{A})=\frac{\exp(U_{j})}{\sum_{k=1}^{n}\exp(U_{k})},$ (1) $$

where $U_{j}$ is the sum of utilities for all items in option $j$.(^2^22We adopt the classic assumption that utilities of multiple items in an option are combined linearly. This may not be true in realistic scenarios, e.g., if someone has an ice cream they are less likely to want a lollipop.)

To make rational inferences about the preferences (utilities $\mathbf{u}$) that motivated the observed choice, the posterior over utilities $p(\mathbf{u}|c,\mathbf{A})$ is inverted using Bayes’ rule:

$$ $p(\mathbf{u}|c,\mathbf{A})=\frac{p(c|\mathbf{u},\mathbf{A})p(\mathbf{u})}{p(c| \mathbf{A})}.$ (2) $$

Put simply, the posterior distribution $p(\mathbf{u}|c,\mathbf{A})$ is computed starting from a prior $p(\mathbf{u})$, scaled by the likelihood of making the choice $p(c|\mathbf{u},\mathbf{A})$, and normalized by the marginal likelihood $p(c|\mathbf{A})$.

Given the utilities, when a rational agent reasons about which observed decision provides more evidence that a decision-maker prefers a certain item, Jern et al. () suggest two prevailing theories that correlate higher with human behavior than others: absolute utility and relative utility. Absolute utility posits that the expected utility of an item $x$ over the posterior distribution directly corresponds to there being more evidence for the decision-maker preferring the item $x$:

$$ $\textnormal{preference}(x)\propto\mathbb{E}[u_{x}|c,\mathbf{A}].$ (3) $$

Meanwhile, relative utility posits that the preference of an item corresponds to the probability that its utility is highest amongst all items:

$$ $\textnormal{preference}(x)\propto p(\forall i,u_{x}>u_{i}|c,\mathbf{A}).$ (4) $$

After measuring the inferences that LLMs make about others’ utilities based on observed decisions, we compare them against the predictions from the absolute utility and relative utility models. In addition, we also compare them against two components of the right-hand expression of Equation 2, the likelihood $p(c|\mathbf{u},\mathbf{A})$ and the inverse of the marginal likelihood $1/p(c|\mathbf{A})$ (henceforth referred to as “marginal likelihood” for simplicity), which correspond to simpler — yet still rational — behavior. By themselves, these components have lower correlations with human behavior, but they serve as important building blocks for the both absolute and relative utility (Jern et al., ).

#### Human data.

We compare LLMs’ outputs against data collected from people performing the original task of ranking the 47 decisions, conducted by Jern et al. (). Jern et al. found that the rational models of absolute utility and relative utility both achieve Spearman correlations of 0.98 with human inferences, outperforming likelihood, marginal likelihood, and feature-based models.

#### Language models.

We ran our LLM experiments on Llama-3-8B, Llama-3-70B, Claude 3 Opus, GPT-4-Turbo (0125-preview), and GPT-4o. Experiments were conducted in April and May of 2024. We set sample sizes of 43 for the positive context and 42 for the negative context, which are equal to the sample size of the original human experiment. This was obtained for all models aside from Claude 3 Opus, where we set an artificial sample size of 5 due to cost constraints. We used temperature = 1 across all models, and prompted using both zero-shot and chain-of-thought prompting. For each sample, we queried the LLM to make $47\choose{2}$ decisions, one for each pairwise comparison.

We constructed the prompts based on the original scripts and text instructions given to participants in the human experiment, adapted to pairwise comparisons instead of ranking 47 decisions at once. We also removed physical details of the experiment (e.g., the decisions were printed on cards with colors to represent items, while we describe items with natural language). The prompt first introduces the context (either candy or electric shocks), describes the pair of observed decisions, and concludes with the request to select the choice that more strongly suggests that the decision-maker prefers the target item. We also included additional experiment clarifications present in the original human experiment, as well as instructions for structuring the outputs (e.g., chain-of-thought) if applicable.
To mitigate effects from LLMs’ positional bias (Wang et al., ) and any potential context biases related to item descriptions, we shuffled the individual contexts we assigned to each item (e.g., black vs. blue candy). We also shuffled the order that decisions appear in the pair, shuffled the order of options appearing within the decisions, and shuffled the order of items listed within each option. A prompt example is in Appendix F.

After LLMs make the pairwise decisions, we parse the answers based on a handmade rule-based classifier. In the chain-of-thought case, if the classifier is unable to categorize the answer, we re-prompt the LLM asking it to classify its response. After we have results for all $47\choose 2$ pairwise comparisons, we aggregate them into a ranking ordered by the number of pairwise wins (ties are considered 0.5). We then compare these rankings against those of humans and rational models.

### 4.1 Results

To investigate the decision-making models behind how LLMs make inferences from observed decisions, we compare the Spearman correlation of LLMs’ inferences against those made by humans and rational models. We organize the results into two main takeaways.

#### Stronger LLMs become highly capable at rational modeling.

The inferences of LLMs have positive correlations with both absolute and relative utility, and that this correlation rises both as model capabilities improve and when models are allowed inference-time reasoning (see Table 3); for the positive CoT case, Llama-3-8B achieves 0.62 correlation with absolute and relative utility, while Llama-3-70B achieves correlations of {0.88, 0.89} and GPT-4o achieves correlations of {0.95, 0.94}.

Though LLMs may have been trained on data from the original experiment, our setup with pairwise decisions, extensive shuffling, and prompt adaptations ensure that LLMs’ prior experiences with Jern et al. () do not help it “cheat” and make more rational choices. Thus, we can attribute high correlations with rational models as evidence that LLMs implicitly assume rationality in this setting.

#### LLM inferences are highly correlated with people.

We also find that LLMs have high correlations with the inferences made by people. GPT-4o with CoT achieves a 0.97 Spearman correlation with human behavior, indicating that it makes inferences about others that are extremely similar to those made by people. We also observe that like humans, LLMs are less consistent with the rational model in the more difficult negative context, and have negative correlations with the likelihood component of rational models.
In the zero-shot positive case, LLMs seem to be much more correlated with marginal likelihood than the more complex rational models, indicating that it may be using this simpler decision-making model as a proxy when given no context to reason about its answer.

We also observe that LLMs’ correlations with human inferences are consistently higher than their correlations with rational models. This is especially true in the negative context where humans are less consistent with the rational model (e.g., GPT-4o with CoT has a 0.87 correlation with humans, compared to a 0.74 correlation with the highest rational model). Thus, although LLMs typically assume rationality, when people’s inferences diverge from those of rational models, LLMs’ inferences are closer to humans. This could be explained by LLMs sharing some heuristic strategies with humans, but future investigations would be required to verify this.
We provide scatterplots illustrating the patterns of responses in Appendix E.

**Table 3: Spearman correlations between inverse decision rankings made by LLMs / humans and predictions from rational models. LLMs that most highly correlate with humans in each setting are in bold. Correlation coefficients with absolute value $\geq 0.3$ are statistically significant at $\alpha=0.05$, and $\geq 0.47$ at $\alpha=0.001$.**
| context | prompt | compared with | Llama-3-8B | Llama-3-70B | Claude 3 Opus | GPT-4-Turbo | GPT-4o | humans |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| positive<br>(candies) | CoT | humans | 0.66 | 0.92 | 0.92 | 0.95 | 0.97 | 1.00 |
| absolute utility | 0.62 | 0.88 | 0.89 | 0.93 | 0.95 | 0.98 |  |  |
| relative utility | 0.62 | 0.89 | 0.92 | 0.94 | 0.94 | 0.98 |  |  |
| likelihood | -0.57 | -0.51 | -0.43 | -0.42 | -0.45 | -0.51 |  |  |
| marginal likelihood | 0.67 | 0.70 | 0.66 | 0.66 | 0.70 | 0.76 |  |  |
| zero-shot | humans | 0.28 | 0.63 | 0.56 | 0.65 | 0.65 | 1.00 |  |
| absolute utility | 0.20 | 0.57 | 0.52 | 0.59 | 0.59 | 0.98 |  |  |
| relative utility | 0.23 | 0.59 | 0.53 | 0.60 | 0.60 | 0.98 |  |  |
| likelihood | -0.62 | -0.68 | -0.56 | -0.52 | -0.74 | -0.51 |  |  |
| marginal likelihood | 0.57 | 0.72 | 0.62 | 0.58 | 0.77 | 0.76 |  |  |
| negative<br>(shocks) | CoT | humans | 0.53 | 0.68 | 0.74 | 0.77 | 0.87 | 1.00 |
| absolute utility | 0.25 | 0.42 | 0.48 | 0.59 | 0.68 | 0.90 |  |  |
| relative utility | 0.40 | 0.57 | 0.59 | 0.63 | 0.74 | 0.93 |  |  |
| likelihood | -0.03 | 0.00 | 0.04 | 0.06 | -0.05 | -0.28 |  |  |
| marginal likelihood | 0.21 | 0.23 | 0.24 | 0.24 | 0.36 | 0.61 |  |  |
| zero-shot | humans | 0.17 | 0.43 | 0.43 | 0.53 | 0.51 | 1.00 |  |
| absolute utility | -0.11 | 0.17 | 0.15 | 0.31 | 0.24 | 0.90 |  |  |
| relative utility | 0.03 | 0.28 | 0.29 | 0.36 | 0.31 | 0.93 |  |  |
| likelihood | -0.06 | -0.09 | -0.14 | 0.12 | -0.09 | -0.28 |  |  |
| marginal likelihood | 0.09 | 0.20 | 0.25 | 0.25 | 0.24 | 0.61 |  |  |

## 5 Discussion

We conducted an extensive evaluation of how LLMs assume people make decisions. In our forward modeling experiments, we found that LLMs struggle to predict or simulate human behavior in a simple risky choice setting, assuming that people make decisions more rationally than we actually do. We connect this to a previous finding in psychology — that people model others as more rational than they are — in order to explain why people think LLMs produce human-like behavior when making decisions. Then in our inverse modeling experiments, we find that LLMs also assume people act rationally when reasoning backwards from observed actions to internal utilities, aligning with how humans make inferences about others’ choices. Thus, LLMs seem to adopt a consistent model of human decision-making across forward and inverse modeling — one that assumes people act more rationally than we actually do.

In the remainder of this section, we cover broader impacts related to aligning LLMs and simulating humans using LLMs, discuss limitations to our work, and conclude with some notable future directions including the potential for cognitive science to help understand LLM alignment.

#### Implications for aligning and training LLMs.

The psychology literature shows that there is a dichotomy between how people make decisions and how we expect others to make decisions. How should alignment be defined when these are different? Existing frameworks that focus on safe and useful deployments (e.g., Bommasani et al., ; Askell et al., ) may prioritize aligning with our expectations, but there are also many merits to having models behave like us (e.g., Park et al., ; Shaikh et al., ). We believe a reasonable answer to this is to separate alignment into two sub-cases: alignment with human expectations and alignment with human behavior, and to train separate models that fulfill each objective. Models aligned with human expectation should shed human tendencies such as resource-rationality, i.e. sacrificing quality to reduce effort (Evans et al., ; Alanqary et al., ; Lieder and Griffiths, ), while models designed to simulate humans should retain them. By recognizing the difference between people’s expectations and behavior, we provide support for developing more specific alignment objectives grounded in insights from social science.

We hypothesize that certain training paradigms may be more suited towards aligning to human expectations, while others favor alignment with human behavior. For instance, high quality written responses used in Supervised Fine-Tuning may teach the LLM to mimic the original human writers, aligning outputs towards human behavior. On the other hand, when humans provide preferences for RLHF in a chat setting, their judgement might reflect what the human rater expects from the LLM.

#### Implications for simulating humans using LLMs.

There is a growing literature investigating whether we can use LLMs to simulate humans for various applications (Park et al., ), such as acting as mock participants in human studies (Argyle et al., ; Aher et al., ; Hämäläinen et al., ), collecting public opinion (Chu et al., ; Kim and Lee, ; Sun et al., ), and helping provide realistic reactions to assist people’s communication (Liu et al., ; Shaikh et al., ; Lin et al., ; Shin and Kim, ). As our experiments show that LLMs make decisions more rationally than people do, and also predict people’s decisions to be more rational than they are, current LLMs may be fundamentally misaligned for the task of simulating human choices. Developing policy recommendations or even designing futher experiments based on the choices that LLMs make may be misleading — similar to the concerns about the use of an overly rational “homo economicus” originally raised by researchers in behavioral economics (Tversky and Kahneman, ; Kahneman and Tversky, ). Instead, we believe different training paradigms must be created to accomodate the divergent training goals of human expectation and behavior.

#### Limitations.

Our experiments focused on a subset of all models and tasks for which we could evaluate models’ rationality. This subset was carefully selected to be representative of the existing literature, but could be expanded upon in future research.
In particular, our experiments used controlled, abstract domains that cannot guarantee generalization to real-world contexts. While this is the same challenge faced by all human experiments, it is uniquely challenging for work done on LLMs due to their black-box nature and sensitivity to input prompts (Sclar et al., ). Furthermore, like all human data, the psychological datasets that we compare LLMs against were potentially subject to sampling bias and it is unknown whether they fully represent the true distribution of human rational choice or inference.

Another limitation is that we only use a simple simulation paradigm when testing LLMs’ capabilities in the risky choice experiments. While we rely on sampling temperature to create variance between individual participant samples, other works have used more advanced methods such as adding personal details, demographics, or traits for more realistic variation (Zhou et al., ; Hu and Collier, ). Instead, our work evaluates the simplest LLM simulation method at scale, and the choices13k dataset did not contain such information regarding subjects, making a representative inclusion of these features impossible. Future work could conduct a more comprehensive analysis of different simulation methods and whether they alter LLMs’ implicit decision-making models.

A third limitation to our work is that we do not directly compare LLM predictions of human decisions with human predictions of the same decisions. The psychology literature has shown that there can be differences between when people perform the risky choice task and when they predict how others would perform the same task:
Faro and Rottenstreich () found that people’s predictions of others’ choices are closer to risk neutrality than choices of their own; when people
are risk seeking, they predict that others will be risk seeking but less so; and where people are risk averse, they predict that others will be risk averse but also less so.
Collecting a dataset of people’s predictions about others’ decisions could allow us to make a quantitative comparison of humans and LLMs in this setting.

#### Future directions.

One peculiar observation that we found in the inverse modeling results is that LLMs’ inferences were consistently more correlated with people than they were with rational models.
This suggests that LLMs may potentially capture aspects of human behavior that are not present in existing theoretical models. While we have focused on using theories and paradigms from psychology to analyze LLMs, there may also be opportunities to use LLMs to refine existing theories about people. More generally, our results show how studies and theories originating in psychology and cognitive science can help quantify the behavior of LLMs. These fields offer many other opportunities to compare the behavior of LLMs with humans, helping us towards understanding (both) these complex yet extremely capable systems.

## Acknowledgements

Experiments were supported by Azure credits from a Microsoft AFMR grant.
This work and related results were made possible with the support of the NOMIS Foundation. Special thanks to Theodore R. Sumers, Jian-Qiao Zhu and Mengzhou Xia for providing invaluable feedback for this project.

## Appendix A Forward Modeling prompts

**Table 4: Zero-shot prompting for forward modeling paradigms**
| LLMs predict individual human participant’s choice: |
| --- |
| A person is presented with two gambling machines, and makes a choice between the machines with the goal of maximizing the amount of dollars received.<br>The person will get one reward from the machine they choose. A fixed proportion of 10% of this value will be paid to the participant as a performance bonus.<br>If the reward is negative, their bonus is set to $0.<br>Machine A: {}<br>Machine B: {}<br>Which machine does the person choose?<br>Do not provide any explanation, only answer with A or B: |
| LLMs predict the human choice distribution: |
| {} people are presented with two gambling machines, and each person makes a choice between the machines with the goal of maximizing the amount of dollars received.<br>Each person will get one reward from the machine they choose. A fixed proportion of 10% of this value will be paid to the participant as a performance bonus.<br>If the reward is negative, their bonus is set to $0.<br>Machine A: {}<br>Machine B: {}<br>How many people choose Machine A?<br>How many people choose Machine B?<br>Please only provide the percentage of people who choose Machine A and Machine B in the json format. |
| LLMs act as human participant: |
| There are two gambling machines, A and B. You need to make a choice between the machines with the goal of maximizing the amount of dollars received.<br>You will get one reward from the machine that you choose. A fixed proportion of 10% of this value will be paid to you as a performance bonus.<br>If the reward is negative, your bonus is set to $0.<br>Machine A: {}<br>Machine B: {}<br>Which machine do you choose?<br>Do not provide any explanation, only answer with A or B: |

## Appendix B Forward Modeling Results with Different Prompts

To investigate how LLMs estimate human behavior for the overall sample sizes, we asked LLMs to predict the probability distribution of human choices between gamble machine A and gamble machine B. We observed that estimate the probablity of the overall decisions mitigates the strong correlation with the maximum expected value of each machine while not improving the correlation with actual human behaviors. Particularly for zero-shot prompting, both closed-source and open-source models in Table 5 show a drop in correlation values between 14% and 25% compared to the zero-shot prompting results in Table 1.

We also observed that even when LLMs are asked to act as human participants in making decisions, their outcomes remain consistently more rational than actual human behavior under CoT prompting. Table 8 shows the results of LLMs’ decisions as individual human participants compared to the maximum expected value. Both GPT-4-Turbo and GPT-4o exhibit a high correlation with the maximum expected value, each with a correlation coefficient of 0.91 and 0.92 and the MSE of 0.045 and 0.037, while still maintaining a moderate correlation with actual human behavior, as shown in Table  7.

**Table 5: The correlation between LLMs predicting the human choice distribution based on the aggregate sample size of participants and the actual human choice.**
|  |  | Llama3-8B | Llama3-70B | GPT-4-Turbo | GPT-4o | Humans |
| --- | --- | --- | --- | --- | --- | --- |
| Zero-shot | Spearman correlation | 0.1045 | 0.2827 | 0.4812 | 0.6156 | / |
| Pearson correlation | 0.1032 | 0.2904 | 0.4830 | 0.6112 | / |  |
| MSE | 0.2811 | 0.2668 | 0.1951 | 0.1633 | / |  |
| CoT | Spearman correlation | 0.1799 | 0.1046 | 0.6208 | 0.5825 | / |
| Pearson correlation | 0.1783 | 0.0992 | 0.6308 | 0.6012 | / |  |
| MSE | 0.2615 | 0.2954 | 0.1202 | 0.1282 | / |  |

**Table 6: The correlation between LLMs predicting the human choice distribution based on the aggregate sample size of participants and the maximum expected value.**
|  |  | Llama3-8B | Llama3-70B | GPT-4-Turbo | GPT-4o | Humans |
| --- | --- | --- | --- | --- | --- | --- |
| Zero-shot | Spearman correlation | 0.0426 | 0.1688 | 0.3380 | 0.1741 | 0.4835 |
| Pearson correlation | 0.0426 | 0.1688 | 0.3380 | 0.1741 | 0.4835 |  |
| MSE | 0.4752 | 0.4361 | 0.3465 | 0.4527 | 0.2580 |  |
| CoT | Spearman correlation | 0.2025 | 0.8406 | 0.8458 | 0.8518 | 0.4835 |
| Pearson correlation | 0.2025 | 0.8406 | 0.8458 | 0.8518 | 0.4835 |  |
| MSE | 0.3978 | 0.0807 | 0.0726 | 0.0702 | 0.2580 |  |

**Table 7: The correlation between LLMs acting as a human participant to make choice and the actual human choice.**
|  |  | Llama3-8B | Llama3-70B | GPT-4-Turbo | GPT-4o | Humans |
| --- | --- | --- | --- | --- | --- | --- |
| Zero-shot | Spearman correlation | 0.4047 | 0.4528 | 0.5841 | 0.4617 | / |
| Pearson correlation | 0.4068 | 0.4559 | 0.5667 | 0.4565 | / |  |
| MSE | 0.0920 | 0.1372 | 0.1414 | 0.2031 | / |  |
| CoT | Spearman correlation | 0.4597 | 0.6223 | 0.6153 | 0.6074 | / |
| Pearson correlation | 0.4600 | 0.6165 | 0.6115 | 0.6030 | / |  |
| MSE | 0.1972 | 0.1621 | 0.1640 | 0.1659 | / |  |

**Table 8: The correlation between LLMs acting as a human participant to make choice and the maximum expected value.**
|  |  | Llama3-8B | Llama3-70B | GPT-4-Turbo | GPT-4o | Humans |
| --- | --- | --- | --- | --- | --- | --- |
| Zero-shot | Spearman correlation | 0.1774 | 0.3130 | 0.4190 | 0.2944 | 0.4835 |
| Pearson correlation | 0.1730 | 0.3069 | 0.4053 | 0.2944 | 0.4835 |  |
| MSE | 0.2888 | 0.2980 | 0.2729 | 0.3536 | 0.258 |  |
| CoT | Spearman correlation | 0.5155 | 0.8353 | 0.9100 | 0.9255 | 0.4835 |
| Pearson correlation | 0.5155 | 0.8353 | 0.9100 | 0.9255 | 0.4835 |  |
| MSE | 0.2492 | 0.0836 | 0.0450 | 0.0372 | 0.258 |  |

## Appendix C Model Correlations for Forward Modeling

Figure: Figure 2: The correlations between LLMs [Llama3-8B, Llama3-70B, GPT-4-Turbo (0125-preview), GPT-4o]
Refer to caption: extracted/5764178/figures/forward/heatmap_combined.png

We provide the full correlation results for the three forward modeling paradigms for Llama3-8B, Llama3-70B, GPT-4-Turbo (0125-preview), and GPT-4o in Figure 2. Compared to the correlations in zero-shot prompting, CoT prompting shows a higher degree of correlation across all four LLMs.

## Appendix D Comparing LLMs to a Wider Range of Behavioral Models

Our results clearly show that chain-of-thought results in LLM responses that align closely with expected value.
To try to understand whether there was a systematic pattern in the responses of the zero-shot models, we fit 18 choice models from the behavioral sciences to the output of GPT-4 using the zero-shot individual choice prompt. The set of models was based on those used in Peterson et al. (), where they are described in more detail together with references to the original paper.

In this context, each model represents a hypothesis about the LLM’s beliefs about human behavior. These included Heuristic models (He et al., ), wherein people are thought to emply mental shortcuts to make decisions, Counterfactual models (He et al., ), which appeal to constructs like regret and disappointment, and Subjective Expected Utility models (He et al., ), which assume that quantities involved (money and probability) are perceived or otherwise treated subjectively. This latter, third category contains many of the most influentual models—Expected Utility Theory (EU) and Prospect Theory (PT)—as well as the model proposed by Peterson et al. () which is called Mixture of Theories (MOT).

**Table 9: MSE between GPT-4-individual-zero-shot outputs and the fitted predictions of behavioral models.**
| Behavioral Model | MSE |
| --- | --- |
| Better Than Average | 0.20473 |
| Equiprobable | 0.20212 |
| Low Payoff Elimination | 0.18248 |
| Low Expected Payoff Elimination | 0.18383 |
| Probable | 0.20559 |
| Minimax | 0.20751 |
| Maximax | 0.20401 |
| Priority Heuristic | 0.18994 |
| Disappointment Theory with EV | 0.16125 |
| Disappointment Theory with EU | 0.12273 |
| Disappointment Theory Without Rescaling | 0.16134 |
| RegretTheory with EV | 0.15918 |
| RegretTheory with EU | 0.12278 |
| Expected Value | 0.16134 |
| Expected Utility | 0.11435 |
| Prospect Theory | 0.11427 |
| Transfer of Attention Exchange | 0.12028 |
| Mixture of Theories | 0.09835 |

Table 9 shows the results. Models in the top two sections of the table (Heuristic and Counterfactual) provided strictly inferior fits compared to Subjective Expected Utility models in the third section. Among those in the third section, Expected Value provided the worst fit. Expected Utility was notably better, suggesting that GPT-4 correctly assumes that people do not treat the value of money objectively / linearly. Prospect Theory improved this score slightly through the incorporation of a subjective probability weighting function, but that fitted function was largely linear, suggesting that GPT-4 incorrectly assumes that people do not treat probabilities subjectively. Lastly, MOT provided the best fit to the inferences of GPT-4. In previous work, MOT also provides the best fit to human data, but the fitted parameters are different (Peterson et al., ). When fitted directly to choices13k, MOT learns a mixture of two utility functions (e.g., like the one in Expected Utility) and two probability weighting functions (e.g., like the one in Prospect Theory). Notably, one of the probability weighting functions is usually linear, and the other S-shaped. In the present case, one of the weighting functions was linear, but the other approximated a flat line. This suggests that GPT-4 expected people to be approximately rational most of the time, but completely ignores probabilities (i.e., weights them equally) in a minority of cases.

## Appendix E Full Results for Inverse Modeling

In this section, we provide the correlation plots between humans/rational models and LLMs. For brevity, we consider only the positive CoT context. The ordering of the plots begins with GPT-4o, followed by GPT-4 Turbo, Claude-3 Opus, Llama-3-70B, and concludes with Llama-3-8B.

Figure: (a) Human vs. GPT-4o $\rho=0.97$
Refer to caption: extracted/5764178/figures/inverse/GPT-4o/positive/cot/human_LLM.png

Figure: (a) Human vs. GPT-4 Turbo $\rho=0.95$
Refer to caption: extracted/5764178/figures/inverse/GPT-4-turbo-0125/positive/cot/human_LLM.png

Figure: (a) Human vs. Claude 3 $\rho=0.92$
Refer to caption: extracted/5764178/figures/inverse/Claude-3/positive/cot/human_LLM.png

Figure: (a) Human vs. Llama-3-70B $\rho=0.92$
Refer to caption: extracted/5764178/figures/inverse/Llama-3-70B/positive/cot/human_LLM.png

Figure: (a) Human vs. Llama-3-8B $\rho=0.92$
Refer to caption: extracted/5764178/figures/inverse/Llama-3-8B/positive/cot/human_LLM.png

## Appendix F Inverse Modeling Prompts

An example inverse modeling prompts is shown in Table 10. First, the context of the experiment is introduced, then the choices are listed, and lastly the LLM is asked to reply with which comparison more strongly suggests that the decision-maker prefers a certain target item.

**Table 10: Example prompt for inverse modeling, zero shot.**
| Prompts for Inverse Modeling: |
| --- |
| The following are two choices that people have made between different bags of candy. Each candy is a different color.<br>Choice 1 was made between the following bags:<br>Bag 1: red, brown, yellow, blue.<br>Bag 2: black.<br>The person making the choice chose Bag 2.<br>Choice 2 was made between the following bags:<br>Bag 1: yellow, black, red, brown.<br>The person making the choice chose Bag 1.<br>People were required to choose among the bags available, and were not allowed to reject all the bags.<br>For example, when there is only one bag, the person has no choice but to choose it.<br>Which choice (1 or 2) more strongly suggests that the person making the choice likes black candies?<br>Please respond with either "Choice 1" or "Choice 2". Do no include anything else in your answer. |

## Appendix G 47 Decisions used in Inverse Decision-Making Experiment

We provide a list of the 47 decisions used in the inverse decision-making experiment of Jern et al. () in Table 11. Columns represent options, and letters represent items with the options. Based on the context, letters were replaced with colored candies or numbered electric shocks. Participants ranked these decisions by their strength in suggesting that the decision-maker preferred item $x$ over the other items.

**Table 11: List of 47 observed decisions from the inverse decision-making experiment of Jern et al. (). Decisions contained between 1-5 options, and each option corresponds to a column. The option in the leftmost column was chosen in all decisions. No options were empty; blank entries indicate that the decision had less than the maximum number of options. Each item is represented using a letter, with $x$ being the target item that inferences are made upon.**
| option 1 | option 2 | option 3 | option 4 | option 5 |
| --- | --- | --- | --- | --- |
| $d,c,b,a,x$ |  |  |  |  |
| $c,b,a,x$ |  |  |  |  |
| $b,a,x$ |  |  |  |  |
| $a,x$ |  |  |  |  |
| $x$ |  |  |  |  |
| $c,b,a,x$ | $d,b,a,x$ |  |  |  |
| $a,x$ | $b,x$ | $c,x$ | $d,x$ |  |
| $b,a,x$ | $c,a,x$ |  |  |  |
| $b,a,x$ | $b,c,x$ | $b,d,x$ |  |  |
| $b,a,x$ | $d,c,x$ |  |  |  |
| $a,x$ | $b,x$ |  |  |  |
| $b,a,x$ | $c,a,x$ | $b,d,x$ |  |  |
| $a,x$ | $b,x$ | $c,x$ |  |  |
| $c,b,a,x$ | $d$ |  |  |  |
| $b,a,x$ | $c$ |  |  |  |
| $a,x$ | $b$ |  |  |  |
| $b,a,x$ | $c$ | $d$ |  |  |
| $b,a,x$ | $d,c$ |  |  |  |
| $a,x$ | $b$ | $c$ |  |  |
| $a,x$ | $b,x$ | $d,c$ |  |  |
| $b,a,x$ | $b,d,c$ |  |  |  |
| $a,x$ | $b,x$ | $c,x$ | $a,d$ |  |
| $a,x$ | $b$ | $c$ | $d$ |  |
| $b,a,x$ | $b,c,x$ | $b,a,d$ |  |  |
| $a,x$ | $b,x$ | $a,c$ |  |  |
| $a,x$ | $c,b$ |  |  |  |
| $c,b,a,x$ | $c,b,a,d$ |  |  |  |
| $a,x$ | $b$ | $d,c$ |  |  |
| $a,x$ | $b,x$ | $a,c$ | $a,d$ |  |
| $a,x$ | $a,b$ |  |  |  |
| $b,a,x$ | $b,a,c$ |  |  |  |
| $a,x$ | $a,b$ | $d,c$ |  |  |
| $a,x$ | $d,c,b$ |  |  |  |
| $x$ | $a$ |  |  |  |
| $b,a,x$ | $b,a,c$ | $b,a,d$ |  |  |
| $a,x$ | $a,b$ | $a,c$ |  |  |
| $a,x$ | $a,b$ | $a,c$ | $a,d$ |  |
| $x$ | $a$ | $b$ |  |  |
| $x$ | $a$ | $b$ | $c$ |  |
| $x$ | $a$ | $c,b$ |  |  |
| $x$ | $a$ | $b$ | $c$ | $d$ |
| $x$ | $c,b,a$ |  |  |  |
| $x$ | $b,a$ | $d,c$ |  |  |
| $x$ | $a$ | $b$ | $d,c$ |  |
| $x$ | $a$ | $d,c,b$ |  |  |
| $x$ | $d,c,b,a$ |  |  |  |

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: - What are some other potential human decision-making models that can also be tested on LLMs, in addition to forward and inverse modeling?
- How are the insights gathered from this study inform the development of LMs applications, or inform better design of the next generation of LMs or other AIs?
- Would you imagine an ideal LMs in the future should align perfectly with humans on these psychology data? As we know that the conclusion of human data very much depend on who these people are. How do you imagine the different participant pool will impact the impact of the results of this study?
soundness: 3
strengths: - The application of psychometric data of human for analyzing LLMs behaviors reveals very interesting insights between the gap of LM vs. human decision-making.
- The study design and the analysis are rigorous.
- The insights of "While we have focused on using theories and paradigms from psychology to analyze LLMs, there may also be opportunities to use LLMs to refine existing theories about people." is thought-provoking.
summary: This paper studies how advanced LLMs make predictions of human decisions, and reveal that frontier LMs assume that people are more rational than we really are. By using established datasets capturing human decisions, this study show that LLMs appear to be aligned with the human expectation that other people will act rationally (much like expected value theory), rather than with how people actually act.
weaknesses: - One might argue that these psychology tests used in this work appear to be quite simplistic to inform practical development of next generation LMs.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: - Why set the temperature to 0.7 for some experiments?
- If I'm reading correctly, the poor predictions in the zero-shot setting aren't explained well by the model incorrectly modeling humans as rational actors? What's your alternative explanation?

> We found that GPT-4 Turbo could not provide a valid output ranking 47 choices at once. Thus, for all LLMs, we obtained rankings by asking the LLM to perform pairwise comparisons across 47choose2 pairs of decisions. Pairwise outputs were limited to {stronger, weaker, tie}, and were aggregated across decisions to form a ranking. Ties were discouraged to capture small differences between decisions

Why even permit the "tie" answer?
soundness: 3
strengths: - applies research from the psychology field
- good discussion of alignment
summary: Mainly the paper prompts LLMs (Llama3, Claude Opus, GPT-4) to predict the human choices in two games (predicting gambling choices and inferring preferences from choices). They find that LLMs poorly predict human behavior in both the zero-shot and COT settings—in the latter, the error is explained well by the models predicting the human behavior to be more rational than it is.
weaknesses: see questions

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: - How do you measure the correlation for task 2 and 3 in the forward modeling task?
- Could there be circular inconsistencies in the pairwise ranking for the inverse modeling task? For instance, if the model ranks decision A > B, B > C, but then C > A, how is this resolved?
- Why not use “average human participant” for the prompts in Table 5 in the Appendix?
- Why did you decide to use “you” and refer this to human simulation? “What would an (average) human participant do?” sounds better?
- In Table 11 in the Appendix, where is Bag 2 in Choice 2? Is it referring to the Bag 2 from above?
soundness: 3
strengths: - The authors explore a very interesting direction and implication in the current LLM alignment research. The current LLM alignment may actually limit the models’ ability to understand genuine human behavior.
- The authors link their work nicely to existing well-grounded cognitive science studies, which will be helpful to other readers who are not familiar with this area.
summary: This paper investigates how LLMs perceive and predict human decision-making. The authors claim that LLMs (e.g., GPT-4 and Llama-3) tend to assume more rationality than people exhibit in reality. They adapt existing design of psychology experiments regarding human decisions, and compared human behavior with LLM predictions in two decision-making tasks: (1) predicting binary decision for gambling and (2) pairwise ranking the decisions regarding how much it tells about the human preference of an object. The experiments show that LLMs are more correlated with rational decisions than actual human behavior. The authors also claim this discrepancy between LLM rational assumptions and actual human behavior may have implications for alignment in AI systems, as well as for designing models that either simulate human behavior or align with human expectations.
weaknesses: - While I also believe LLMs struggle to model irrationality, I’m not entirely convinced that the current experiment design fully supports this claim.
    - In the forward modeling section, the binary choice questions have an objectively optimal answer. Why should we conclude that the models are “assuming humans are more rational” simply because their answers are closer to this optimal? A simpler interpretation could be that the models are simply solving problems optimally, regardless of the prompt’s content.
    - The fact that the forward modeling part relies on binary choice questions amplified my concern. For instance, consider a set of binary math questions that are challenging for humans, where we ask the model to predict which answer a person might choose. If the model consistently picks the correct answer more often than humans, would it be valid to conclude that the model assumes humans are more rational than they actually are? Or is the model simply aiming to answer correctly?
- The forward modeling results in Table 1 & 2 indicate that models are more aligned with human choices than with maximum expected values in the zero-shot setting, but become more aligned with maximum expected values only when using CoT. Why, then, does this lead to the conclusion that “LLMs assume people are more rational than we really are”?

---

confidence: 4
contribution: 3
initial_rating: 8
presentation: 4
questions: Based on the weakness above:

* What does the CoT prompt look like? Only the zero-shot prompt is reported in the appendix.
* What is the effect of changing the temperature? Especially, how do the most popular temperature settings (when known) compare to temperature settings that are less common to use?
* What is the effect of changing the prompt? Did the authors try many different prompts to arrive at their results, or do they think the results are stable if one changes the prompt?
soundness: 3
strengths: * **Topic is important.** It is important to get insight in the decision process of LLMs, which has important consequences for how trustworthy we deem their outputs. This paper approaches this question from a CogSci angle.
* **Paper invites follow-up work.** The paper sets the stage for follow-up work on alignment. What it means to align well with humans, depends on whether one wants to align to human expectations, or human behavior, according to the authors.
* **Experiments are mostly solid.** The authors conduct experiments over a wide variety of popular LLMs, and compare the results. This gives insights in how the results generalize over different LLMs. Moreover, the experimental setup is strongly grounded in well-known experiments in Cognitive Science.
summary: This paper compares the implicit decision-making process of LLMs with the human decision making process and finds that LLMs assume that people are more rational than they are, which is in line with what humans expect from other humans.

The paper uses two test suites: (1) Forward Modeling, which is a task in which the LLM needs to predict how likely people would select gamble A over gamble B, (2) Inverse Modeling, in which the LLM needs to predict people’s preferences from a list of choices. The authors compare the outputs of several LLMs with reported human answers to come to their conclusions.

The authors argue that the results have important implications for alignment, namely that alignment should be divided into two cases: alignment with human expectations, and alignment with human behavior.
weaknesses: * **Unclear what the effect of the temperature setting is.** The authors briefly mention their choices for different temperature settings, but not in a lot of depth. Currently it is unclear how robust the findings are against different temperature settings (which would affect the outcomes). See questions below.
* **Unclear what the effect of different prompting strategies is.** The authors report the zero-shot prompt used, and share some of their considerations for designing the prompt. However, as LLMs can be sensitive to the actual prompt used, it would be good to understand how robust the findings are when different prompts are used, beyond the zero-shot vs. CoT. See questions below.

Meta-review: PC is entering meta-review on behalf of SAC/AC:

The reviewers felt that the paper was an interesting research direction, and had important implication in LLM alignment research demonstrating the limitations of models’ ability to understand genuine human behaviour.

Decision: Accept (Poster)