# Toward a computational understanding of bribe-taking behavior

Shiwei Qiu, Yancheng Tang, Hongbo Yu, Hanbo Xie, Jean-claude Dreher, Yang Hu, Xiaolin Zhou

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Shiwei Qiu, Yancheng Tang, Hongbo Yu, Hanbo Xie, Jean-claude Dreher, et al. Toward a computational understanding of bribe-taking behavior. Annals of the New York Academy of Sciences, 2025, 1545, pp.5 - 15. 10.1111/nyas.15294. hal-05357030

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DOI: 10.1111/nyas.15294

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ANNALS OF THE NEW YORK ACADEMY OF SCIENCES

# Toward a computational understanding of bribe-taking behavior

Shiwei Qiu<fnref n="1" />, #

<figure><img src="image_2.png" /></figure>

Yancheng Tang<fnref n="2" />, #

<figure><img src="image_3.png" /></figure>

Hongbo Yu<fnref n="3" />

<figure><img src="image_4.png" /></figure>

Hanbo Xie<fnref n="4" />

<figure><img src="image_5.png" /></figure>

Jean-Claude Dreher<fnref n="5" />

<figure><img src="image_6.png" /></figure>

Yang Hu<fnref n="6" />

<figure><img src="image_7.png" /></figure>

Xiaolin Zhou<fnref n="2" />, <fnref n="7" />

<figure><img src="image_8.png" /></figure>

### Correspondence

Yang Hu, School of Psychology and Cognitive Science, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Center for Psychological Research on Anti-Corruption, East China Normal University, Shanghai 200062, China. Email: yanghu@psy.ecnu.edu.cn

Xiaolin Zhou, School of Psychology and Cognitive Science, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, East China Normal University, Shanghai 200062, China. Email: xz104@psy.ecnu.edu.cn

### Funding information

National Natural Science Foundation of China, Grant/Award Numbers: 32200853, 71942001; STI2023-Major Projects, Grant/Award Number: 2021ZD0200500; National Science Foundation of Shanghai, Grant/Award Number: 23ZR1418400; Fundamental Research Funds for the Central Universities, Grant/Award Number: 2022ECNUXWK-XK003; Agence Nationale de la Recherche, Grant/Award Numbers: ANR-16-IDEX-0005, ANR-11-LABX-0042, ANR-11-IDEX-007, ANR-21-CE37-0032

### Abstract

Understanding how corrupt behavior occurs is a critical issue at the intersection of behavioral ethics, social psychology, and other related social sciences, laying the foundation for establishing effective anticorruption policies. Despite a substantial body of studies focused on bribe-taking behavior—a typical form of corruption—and its modulators, its underlying psychological processes remain poorly understood. Drawing inspiration from recent literature on neuroeconomics and moral decision-making, we argue that bribe-taking decision-making involves a value-based computational process that can be characterized by a computational framework. We show how this framework advances our understanding of bribe-taking decision-making by (1) clarifying how the cost-benefit tradeoff determines the decision to accept or reject a bribe and its neural foundations, (2) improving the prediction of bribe-taking behaviors across contexts and individuals, and (3) enhancing our comprehension of individual differences in bribe-taking behaviors. Moreover, we delineate how this framework can benefit future research on bribery by examining the mechanisms through which various modulators impact the bribe-taking behaviors or the computational processes underlying more intricate forms of corrupt behaviors. We also discussed its potential fusion with artificial intelligence techniques in offering insights for understanding cognitive processes underlying bribe-taking behaviors and designing anticorruption strategies.

### KEYWORDS

anticorruption, bribe-taking, computational modeling, corrupt behavior, value-based decision-making

<footnote n="1">¹School of Psychology and Cognitive Science, East China Normal University, Shanghai, China</footnote>

<footnote n="2">²Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China</footnote>

<footnote n="3">³Department of Psychological & Brain Sciences, University of California Santa Barbara, Santa Barbara, California, USA</footnote>

<footnote n="4">⁴School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA</footnote>

<footnote n="5">⁵Neuroeconomics Lab, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Bron, France</footnote>

<footnote n="6">⁶School of Psychology and Cognitive Science, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Center for Psychological Research on Anti-Corruption, East China Normal University, Shanghai, China</footnote>

<footnote n="7">⁷School of Psychology and Cognitive Science, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, East China Normal University, Shanghai, China</footnote>

<footnote n="9">⁹Shiwei Qiu and Yancheng Tang are co-first authors.</footnote>
# INTRODUCTION

Corruption is one of the most challenging issues worldwide. It encompasses a range of immoral (and often illegal) behaviors generally defined as “the abuse of entrusted power for private gains.”¹ Among these manifestations, bribery stands out as one of the most common forms of corruption, with two essential features. For one, bribery occurs in interpersonal contexts, often through interactions between a powerholder and one or more bribers. For another, bribery involves unethical concerns that point to immoral collaboration between stakeholders (e.g., collaborative cheating) and harm to the interests of third parties (e.g., public welfare²). Previous research on corruption or bribery has mainly focused on revealing the determinants influencing corrupt practices and evaluating effective measures to curb such behaviors from a macro-level perspective (e.g., societal level), often utilizing existing panel data.³⁻⁵ In the past decades, studies in behavioral economics and social psychology have primarily focused on developing lab-based paradigms⁶ and identifying various situational and personality factors⁷ that promote or curb bribery-related behaviors. Based on such empirical evidence, researchers have either provided a global overview of these modulators of corrupt behaviors⁷ or offered an account of corrupt decision-making based on social dilemma principles.⁸ However, the psychological processes⁹ underpinning bribe-taking behaviors remain poorly understood.ᵇ The present article aims to present a novel approach to understanding the cognitive (psychological) processes underlying bribe-taking behaviors, which we argue can be effectively explored through the lens of a value-based computational framework.

# UNDERSTANDING BRIBE-TAKING BEHAVIORS THROUGH THE LENS OF A VALUE-BASED COMPUTATIONAL FRAMEWORK

Inspired by classical cost-benefit models of corruption⁴,⁹ and recent literature on neuroeconomics¹⁰,¹¹ and moral decision-making,¹²,¹³ this framework assumes that the decision of whether to take or refuse a bribe involves a value-based computational process (Figure 1). In particular, a powerholder usually needs to represent the potential benefits (e.g., financial rewards) and costs (e.g., consideration of violating moral principles or risk of being punished for taking a bribe) of taking or refusing a bribe,⁸ thus calculating the subjective value (SV) for each action by assigning differential weights to these benefits and costs (Table 1), which might be influenced by various contextual and individual factors (see the first and second subsections of Future Directions below), and integrating them in a certain manner. By comparing the SV of each option on a common scale, the option with a higher SV is likely to be selected. For instance, if the SV of accepting the bribe outweighs that

of not taking it, the powerholder is more likely to take the bribe. In empirical studies on relevant topics (e.g., other forms of moral behaviors), these computational processes could be revealed by leveraging an optimal experimental task (Box 1) and cognitive computational modeling (Box 2), and are underpinned by a large brain network.¹⁰,¹²,¹⁴ Note that employing a value-based computational framework does not necessarily preclude the use of alternative decision-making models, such as heuristics¹⁵,¹⁶ or a dual-process model,¹⁷,¹⁸ which may also be valuable in explaining the cognitive processes underlying bribe-taking behaviors.ᶜ

This value-based computational framework can be extended by integrating with the drift-diffusion model (DDM) and its variants³² to better understand the temporal dynamics of the value-based tradeoff process underlying bribe-taking decision-making. While DDMs have been widely applied in cognitive psychology³² and decision science,³³ they have yet to be used in research on corrupt behaviors. Compared to the original framework (i.e., standard utility model), DDMs assume that decisions are driven by relative evidence for one option (versus an alternative) that have accumulated over time. In other words, DDMs jointly consider choice variability and reaction time, thereby providing a more comprehensive picture of the cognitive processes underlying bribe-taking decision-making. The key link between the value-based computational framework and DDMs lies in the drift rate parameter. In particular, this parameter in DDMs closely reflects value-based computation during bribe-taking decisions, which can be reformulated as a combination of weighted benefits and costs, as previously discussed. Moreover, DDMs include additional parameters that represent distinct cognitive processes. Specifically, the starting point parameter accounts for an internal bias toward a specific response (i.e., accept or reject a bribe), while the decision-threshold and non-decision time parameters both are concerned with the temporal solution to the choice (i.e., the duration required to decide whether to take or refuse a bribe). Beyond these advantages, integrating our computational framework with DDM brings extra benefits. For instance, a variant of DDMs (e.g., multi-attribute time-varying DDM³³,³⁴) can be used to examine the prioritization of processing certain information (attributes) over others, such as whether a powerholder prioritizes the process of personal profits compared to the process of evaluating the potential loss of a third-party.

This computational framework can advance our understanding of bribe-taking behaviors (and more complex forms of corrupt behaviors) in the following ways. Specifically, it helps elucidate how specific benefits and costs are combined to determine the decision regarding whether to accept or reject a bribe. Moreover, it allows the quantification of the psychological components involved in bribe-taking decision-making, thus improving the prediction of bribe-taking

<footnote>ᵃ Computational processes here refer to psychological processes associated with bribe-taking behaviors, generally encompassing the representation and integration of various costs and benefits into a subjective value for the decision to take (or refuse) a bribe. These processes can be characterized by mathematical formulations (i.e., computational models).
ᵇ See Liu et al.⁹¹ for a conceptually related perspective of bribe-giving behavior.</footnote>

<footnote>ᶜ In general, the value-based computational framework can be seen as a detailed approach describing decision-making typically involving comprehensive evaluations and careful calculations. In contrast, heuristic models simplify the decision process by highlighting partial information (e.g., easily accessible or salient cues), making decisions faster and cognitively less demanding. Dual-process decision model identifies two distinct systems, a fast, intuitive system and a slow, rational system, broadly aligning with heuristic models and the value-based framework, respectively. However, further discussion of the differences between these models in explaining corrupt decision-making processes goes beyond the scope of this article.</footnote>
<figure><img src="image_9.png" /><figcaption>**FIGURE 1** The value-based computational framework describing bribe-taking behaviors. When deciding whether to take a bribe, a powerholder is assumed to engage in a value computation process that weighs multiple benefits and costs, both of which can be divided into material and nonmaterial ones. By assigning different weights to these features and integrating them in one's own way, the powerholder forms a subjective value (SV) for the option of taking or refusing a bribe and reaches a decision by comparing those SVs. Various contextual factors (e.g., culture and norms, interpersonal relationships, social emotions, and power) can modulate decision weights on components involved in the SV computation regarding the cost-benefit tradeoff (indicated by the shaded line toward the component). These modulators can either increase or decrease the decision weight placed on specific components, ultimately influencing the final decision.</figcaption></figure>

<table><caption>**TABLE 1** Potential benefits and costs related to bribe-taking.</caption><thead><tr><th></th><th>Material ones</th><th>Nonmaterial ones</th></tr></thead><tbody><tr><th>Potential benefits</th><td>Monetary rewards and luxury gifts<sup>92,93</sup><br>Sexual favors (i.e., engaging in improper sexual relationships with low-status bribers)<sup>95</sup></td><td>Psychological needs for pursuing power<sup>94</sup><br>A good relationship with the briber or readiness to establish one<sup>96,97</sup></td></tr><tr><th>Potential costs</th><td>Legal penalties resulting in monetary losses or loss of position and power<sup>98-101</sup></td><td>Moral costs from immoral collaboration<sup>102</sup> or harm inflicted on third parties<sup>22,23,103</sup><br>Crisis in social reputation<sup>104,105</sup></td></tr></tbody></table>

behaviors across contexts and individuals. Last, but not least, this framework offers insights into understanding individual differences in bribe-taking behaviors and its link to moral-related personality traits, thereby informing the selection of less corrupt-leaning officials.

## Helping elucidate the cost-benefit computation mechanisms and its neural foundation underlying bribe-taking behaviors

Guided by this computational framework, a recent study³⁵ provided initial evidence regarding the value computation involved in bribe-

taking decision-making and its neural correlates by combining a novel game-based paradigm, computational modeling, and functional magnetic resonance imaging (fMRI). In brief, participants in the MRI scanner played the role of a powerholder and decided whether to accept or reject a series of bribes from different fictitious proposers, aiming for larger profits. In some cases, accepting the bribe additionally harmed the interest of an innocent third party (Figure 3A). By parametrically varying the bribery-related profits and financial costs, as well as distinguishing the means via which proposers earned the money (Figure 3B), the task incorporated the moral cost of immoral collaboration and third-party costs.³⁵ To quantify the value computation involved in bribe-taking decision-making, researchers proposed
## BOX 1 Optimal experimental paradigms

Previous research has utilized two classes of paradigms to probe bribe-taking behaviors in experimental settings (Figure 2). The vignette-based paradigm, primarily adopted by social psychologists, presents participants with a hypothetical scenario of bribery, requesting participants to imagine themselves in the role of the powerholder and make hypothetical decisions.¹⁹⁻²¹ For example, participants acted in the role of the powerholder and rated their intention to take a bribe in a hypothetical scenario where an individual who violated traffic regulations sought to evade punishment by offering a bribe to the powerholder.²¹ While the vignette-based paradigm has provided a qualitative understanding of the factors that influence bribe-taking behaviors, it has limitations in generalizing these research conclusions based on hypothetical decisions to real-world bribe-taking behaviors. The game-based paradigm emerges from behavioral economics and relies on interpersonal interactive tasks. Specifically, participants and the co-players in the game are paid out for real according to their actual behaviors in the task.²²⁻²⁵ It is relatively easy to improve the game-based paradigm by systematically manipulating key variables in a parametric manner, for example, varying the monetary reward and physical pain along a continuum rather than in discrete categories, see Ref. 26. This feature could be combined with cognitive computational modeling (Box 2) to quantify the cognitive processes underlying bribe-taking behaviors.

## BOX 2 Cognitive computational modeling

Cognitive computational modeling refers to the technique of using formal mathematical notations to uncover information processing in a fine-grained manner. Unlike standard analytic approaches used in psychology focusing on the presence of an experimental effect, cognitive computational modeling allows the researchers to quantify the way choice-related attributes are integrated into a decision, thereby improving the understanding of the latent subcomponents underlying decision-making. Furthermore, this approach facilitates the quantitative test of competing hypotheses via formal model comparison and helps generate more precise predictions of behaviors.²⁷

Cognitive computational models have long been used in the research of basic cognition (e.g., perception), economic decision-making, and reinforcement learning. This approach has recently been used in the social and moral domain.¹²,¹³,²⁷⁻³¹ For example, Crockett and colleagues²⁶ developed several competing models based on different theoretical assumptions about processing personal gains and harm to best characterize the subjective value computation process during decision-making. Through formal model comparison, the best-fitting model essentially assumes that a parameter called harm aversion (i.e., describing how an individual feels aversive to harming another person) arbitrated how individuals weigh the harm to another person relative to their own profits.

and compared a series of cognitive computational models following the computational modeling routine.³⁶ The best-fitting model showed that, when deciding whether to accept or reject a bribe, powerholders not only considered the payoffs for themselves and those for the briber, often denoted in social decision-making, but also considered two types of moral costs specifically related to bribe-taking, as mentioned above.

Researchers further explored how this value computation was implemented in the brain. In particular, these moral costs were encoded by dissociated brain regions and integrated with other components in the model into an SV in the ventromedial prefrontal cortex (vmPFC). Moreover, variations in the bribery-specific signal in the dorsolateral prefrontal cortex (dlPFC) represented the differences in preference patterns for bribery-related moral costs (characterized by relevant parameters) among individuals (Figure 3C). Using a brain stimulation technique, a follow-up study further revealed the causal role of the dlPFC in influencing bribe-taking behaviors, possibly by reshaping the moral cost of immoral collaboration.³⁷ Future work can combine this framework, DDMs, and high-temporal resolution neural measurements (e.g., electroencephalogram) to unveil the neural dynamics of cognitive processes underlying bribe-taking decisions. Together, this computational framework lays a foundation for understanding psy-

chological processes and their neural substrates involved in real-life bribe-taking behaviors, which could be far more complicated.

## Improving the prediction of bribe-taking behaviors across contexts and individuals

In real-world situations, powerholders may be confronted with bribe offers in various scenarios. It is easy to predict that a powerholder would possibly forgo a bribe offering very few monetary interests (e.g., a day's salary) but having a high potential cost (e.g., a 10-year imprisonment or the death of another person) based purely on our intuition grounded in cost-benefit tradeoff theory. However, relying solely on such conceptual or verbal models makes it challenging to predict how likely a powerholder is to accept the bribe offer as personal benefits increase while potential costs decrease. In contrast, this can be effectively addressed by implementing this computational framework.¹⁰⁻¹² By fitting sufficient behavioral responses (choices or decision times) of a given sample (e.g., healthy adults) in diverse bribery scenarios (e.g., those varying in monetary profits and imprisonments) to the computational framework, robust estimates of parameters at the group level
<figure><img src="image_10.png" /><figcaption>**FIGURE 2** Two classes of paradigms employed in laboratory experiments investigating bribe-taking behavior. (A) An example of a vignette-based paradigm from Ref. 21. Participants are required to imagine a real-life scenario and make decisions in the role of the person in power. (B) An example of a game-based paradigm from Refs. 23, 24. In a multistage game, participants are assigned different roles (i.e., the private citizen, the public official, or another member of society). Those in the role of the private citizen decide whether to propose a bribe, while those in the role of the public official decide whether to take a bribe at certain stages. The final payoff for each role is represented by the corresponding color at the end of the decision tree.</figcaption></figure>

can be obtained to quantify the psychological components involved in the decision-making processes.³⁸ This enables the framework to quantitatively characterize how likely a representative individual from this sample is to accept or refuse a bribe offer under specific contexts. When multiple alternative models derived from this framework are proposed to explain behaviors, formal model comparison can identify the one that best fits the data.³⁹ When sufficient behavioral data are available for a given individual, this approach can also be applied at the individual level to obtain parameters characterizing how that person weights particular benefits and costs during bribe-taking decision-making. This allows for a more precise prediction of an individual's behavior in a given context. These advantages collectively contribute to improving the predictive accuracy of this framework.

## Fostering the understanding of individual differences in bribe-taking behaviors

Individuals exhibit significant variability in corrupt behaviors: some powerholders successfully resist the lure of bribes, while others succumb to it. Existing evidence suggests that such individual differences are highly dependent on morality-related personality traits. Some traits are positively associated with bribe-taking behaviors (e.g., dispositional greed⁴⁰), while others show the opposite patterns (e.g., honesty-humility and guilt-proneness⁴⁰⁻⁴²). The computational framework further aids in unveiling the nuanced relationship between these traits and parameters characterizing psychological components, thereby improving our understanding of hidden associations between

personality traits and various motivations driving bribe-taking behaviors. Indeed, by leveraging cognitive computational modeling and a parametric design, a recent study revealed that highly guilt-prone powerholders exhibited greater concern for others' suffering, offering a mechanistic explanation for the link between guilt-proneness and bribe-taking behaviors.⁴² From a practical perspective, these trait-parameter relationship findings may provide valuable insights for designing more efficient personality surveys to identify corrupt-prone officials.

## FUTURE DIRECTIONS

### Enhancing the understanding of the potential mechanisms through which various modulators influence bribe-taking behaviors

Previous evidence suggests that bribe-taking behaviors can be highly context-dependent.⁷ Rather than providing a systematic review of various factors that influence bribe-taking behaviors, here we take four prominent contextual modulators—culture and norms, interpersonal relationships, social emotions, and power—as examples. Note that this computational framework, especially in its current simplified form as outlined above, may not encapsulate the entirety of the modulatory influences. Instead, the main goal of this section is to showcase how this computational framework provides insights into the cognitive operations underlying these modulations (Figure 1), thereby advancing our understanding of the potential mechanisms through which various modulators impact bribe-taking behaviors.
<figure><img src="image_11.png" /></figure>

**FIGURE 3** Illustration of the game-based paradigm used in the study by Hu and colleagues.³⁵ (A) An example of the *Bribery* condition in the *Dyad* scenario. Participants in the fMRI scanner played the role of powerholder and decided whether to accept the bribe from a fictitious proposer, who wanted to earn the monetary reward he/she failed to obtain in the Game of Chance due to a wrong guess. Accepting the bribe would also incur a financial cost for an innocent third party. (B) Illustrations of the decision screen of an example trial in the *Bribery* and *Control* conditions. (C) Neuro-computational basis of bribe-taking behavior. The computational model denotes how subjective values (SVs) were computed in the *Dyad* scenario. In a given trial, SV denotes the subjective value. *Gain*<sub>PH</sub> and *Loss*<sub>T</sub> represent the monetary gains for the powerholder (PH; participants) and the financial losses to the third party (T), respectively, given different choices. *Payoff*<sub>other</sub> refers to additional other related payoffs (i.e., the monetary gains for the proposer and the absolute payoff difference between the proposer and the powerholder). &beta; captures the weight of the payoff of the powerholder. &theta; describes the moral cost incurred by collaborating with the fraud committed by a proposer, and &omega; captures the moral cost incurred by harming the interest of a third party. &gamma; accounts for weights on additional other-related payoffs. Note that the model shown here was modified for simplicity in the illustration. On the neural level, the moral cost of monetary gains for the powerholder and the moral cost of third-party losses were encoded in the vAI and TPJ/pSTS, respectively. The model-based SV of accepting (over rejecting) the bribe was associated with the vmPFC. Variations in the bribery-specific signal in the dIPFC represented the differences in preference patterns for bribery-related moral costs (&theta; and &omega;) among individuals. Abbreviations: dIPFC, dorsolateral prefrontal cortex; pSTS, posterior superior temporal sulcus; TPJ, temporoparietal junction; vAI, ventral part of anterior insula; vmPFC, ventromedial prefrontal cortex.

## Cultural (social) norms

Previous studies suggest that individuals are more likely to take a bribe when interacting with people from countries (or regions) notori- ous for a culture of corruption (reflected by the corruption perception index)⁴³⁻⁴⁵ or to engage in bribe-offering when bribery is perceived as prevalent.⁴⁶⁻⁴⁹ Building on this computational framework, we argue that these norms may amplify the weights placed on material gains, while diminishing the value of moral costs during bribery decision- making.

## Interpersonal relationships

Existing evidence indicates that powerholders are more inclined to accept bribes from individuals with whom they share a direct or indi- rect interpersonal relationship.⁵⁰⁻⁵² In our framework, it is plausible to hypothesize that the powerholder may assign greater weight to the profit of a personally associated briber while reducing the weight of moral costs in the bribery decision-making process. Moreover, this framework could further explore how interpersonal relationships mod- ulate the psychological components (e.g., personal profits and unequal
payoffs in the bribe offer between the powerholder and the briber) by employing a hyperbolic or exponential value function, as drawn from the well-established theories in delay discounting research.⁵³⁻⁵⁵

## Social emotions

Recent evidence highlights a pivotal role of social emotions (e.g., guilt, shame, gratitude, etc.) in modulating various social behaviors (e.g., prosocial or reciprocal acts, and immoral conduct⁵⁶,⁵⁷). However, their influence on bribe-taking behaviors and the cognitive underpinnings remain largely unexplored.⁵⁸,⁵⁹ The present framework inspires several predictions. In particular, when a public official feels gratitude or indebtedness toward a briber for past favors,⁶⁰⁻⁶² he/she is more likely to down-weight the cost associated with the immoral collaboration through a bribery transaction as a form of reciprocity, thereby driving the choice to take a bribe. Conversely, anticipated feelings of shame for engaging in corruption or pride in maintaining integrity⁶³ could enhance weights assigned to the moral costs of harming others and the reputation gains, leading the official to reject the bribe.

## Power

While previous research has suggested that power (or the sense of power) changes the way people perceive monetary reward and the social world,⁶⁴⁻⁶⁸ so far little is known about how power shapes bribe-taking behaviors and its underlying cognitive processes. Within this framework, we can predict that individuals with greater power (or a heightened sense of power) may place greater emphasis on the potential financial benefits of accepting a bribe⁶⁹,⁷⁰ while concurrently undervaluing the moral costs associated with the negative impact on others.

## Exploring the computational process and enhancing prediction of bribe-taking (and other complex forms of corrupt behaviors) in real-life context

The current research on bribe-taking has largely overlooked the computational mechanism underlying complex interpersonal interactions between the powerholder and the briber. By incorporating social learning models,¹²,³⁰,⁷¹ this computational framework has the potential to characterize intricate social learning processes, particularly those related to value updating and belief updating.

Specifically, a class of reinforcement learning models, the Rescorla–Wagner model⁷² and its variants, is commonly used to describe how an individual updates the representation of choice values guiding future decision-making via prediction errors, namely, the difference between the actual and expected outcomes. In the moral domain, for instance, these models have been used to account for how individuals learn to benefit others⁷³ or avoid harming others.⁷⁴ In the context of bribery, researchers could use these models to explore how

powerholders learn the bribery-related risk of being punished. For example, if a powerholder is caught taking bribes and punished, the SV of taking a bribe in the subsequent instance would be evaluated lower. In addition, Bayesian models, another class of learning models, are widely used to account for belief updating in social interactions. These models assume that an individual's belief about the environment or stimulus (a presumed probability, "prior") is continuously updated based on the individual's observation of behavior, resulting in an unobservable mental state (an updated probability, "posterior").⁷⁵ Previous research has employed these models to describe how individuals learn others' moral character under uncertain social contexts.⁷⁶,⁷⁷ In the case of bribery, these models could be combined with economic bargaining models to explain the belief updating processes,⁷⁸ such as the powerholder's belief about the briber's tendency to give a bribe or vice versa, during repeated interactions. Moreover, these models can be extended with additional computational components (e.g., dynamical system⁷⁹) for use in multi-person contexts.⁸⁰,⁸¹ In this way, the modified model may offer key insights into how bribery takes place and becomes contagious within a group.

Another limitation of the present framework is that it focuses on bribe-taking decisions with short decision times, common in controlled laboratory settings. This framework might not be very useful in accounting for real-world bribery that often involves prolonged deliberation periods. One potential way to bridge this gap is to integrate the present framework with DDMs and other modeling approaches, allowing for the modeling of computational processes that evolve over time. Moreover, adapting the current cognitive model to incorporate parameters reflecting how real-world complexities influence basic cost–benefit tradeoffs (e.g., the history of interactions between individuals and peer influences), could better capture the dynamics of naturalistic bribery settings. Additionally, it would be beneficial to validate the refined framework with more realistic scenarios to further enhance its applicability and relevance.

## Using large language models to understand the computational nature underlying bribe-taking behavior

One potential application of large language models to complement our computational framework is to incorporate the think-aloud paradigm⁸² into the bribery task. In particular, participants need to articulate their reasoning during each bribe-taking decision trial, providing real-time insights into the cognitive processes underlying bribe-taking decision-making, such as how they evaluate various benefits (e.g., monetary profits) and costs (e.g., moral costs, reputational risks, or legal consequences) that lead to their final choice.⁸³ These verbalizations are transformed into embeddings using natural language models (e.g., BERT, GPT, or LLaMA2). A neural network (NN) is then trained to take these think-aloud text embeddings as input and generate parameters defined in the original computational model for each trial as output. During the training period, the NN learns the text-parameter mapping by optimizing over observed decisions (e.g., accept
or reject bribes) and their associated verbalization patterns. Once the model is trained, the model takes think-aloud embeddings as input for each trial and infers the corresponding cognitive parameters for that trial and participant. These trial-specific parameters are fed into the cognitive model, which predicts the participant's decision. This approach not only enables the cognitive model to perform trial-by-trial estimations with better predictive performance on behaviors but also captures the intricate mapping between natural language from think-aloud data and underlying cognitive processes.

## Aiding artificial intelligence-based anticorruption intervention strategies with the computational framework

This computational framework with existing empirical findings makes it possible to develop anticorruption intervention policies targeting specific mechanisms underlying bribe-taking. Unlike traditional interventions that solely focus on behaviors, computation-based interventions break down the bribe-taking behavior into subcomponents, allowing researchers to understand the different motivations that may drive the same behavior.⁸⁴ For instance, some powerholders may be inclined to take a bribe because they are more sensitive to personal gains or less concerned about jeopardizing others' or public interests. Tailored intervention policies with artificial intelligence (AI) assistance might involve identifying such persons and presenting typical or animated posters that associate material profits with aversive stimuli, as some antismoking campaigns have done, aiming to decrease the individuals' sensitivity to monetary rewards. Alternatively, we may design AI-driven tools to provide timely reminders on the long-term negative consequences of corruption on society (e.g., the harm to the health of the next generation) to increase the individual's awareness of moral costs. Future studies should test and compare the effectiveness of various intervention policies in influencing the computational subcomponents of bribe-taking (e.g., personal gains and various moral costs), thereby leading to effective efforts in anticorruption intervention design.

More broadly, revealing computational processes underlying bribe-taking behaviors can contribute to AI alignment and help design improved prompt systems to promote the ethical behaviors of AI agents (e.g., by reducing unethical responses while capturing the flexibility of the human moral mind⁸⁵). AI acting as moral agents facing moral dilemmas or resource allocation problems essentially involves a value alignment issue.⁸⁶ Tackling this issue requires defining the moral goals and priorities for the AI to pursue, which is challenging due to the complexity of real-life situations. Computational models of moral decision-making provide a promising solution, enabling not only the quantification of representative moral intuitions about particular actions but also the formalization of moral principles about general actions. This, in turn, assists in developing general-purpose algorithms for selecting ethical actions in complex scenarios.⁸⁷,⁸⁸ We are optimistic that with the continued development and integration of various computational tools, more ethical AI agents will be produced

and applied in real life. Collaborating with these ethical AI agents will improve our capabilities in the fight against corruption.⁸⁹,⁹⁰

## CONCLUSION

How to effectively curb bribe-taking behaviors has long been a critical concern for both academia and society. Nevertheless, a theoretical framework dissecting the mechanism of bribe-taking behaviors is still lacking. Here, we propose a computational framework to view bribe-taking as a value-based decision. This framework not only has the advantage of providing a mechanistic account of how the bribe-taking decision is made but also has the potential to explain more intricate corrupt behaviors and to aid in developing morally aligned AI anticorruption systems. Ultimately, we hope this framework deepens our understanding of corruption and offers valuable insights into the development of more effective anticorruption policies.

## AUTHOR CONTRIBUTIONS

S.Q. and Y.T.: Conceptualization; writing—original draft, review, and editing. H.Y., H.X., and J.-C.D.: Writing—review and editing. Y.H.: Conceptualization; writing—original draft, review, and editing; supervision; and funding acquisition. X.Z.: Writing—review and editing; supervision; and funding acquisition.

## ACKNOWLEDGMENTS

This work was supported by research grants from the National Natural Science Foundation of China awarded to Y.H. (32200853), X.Z. (71942001), and the STI 2023 – Major Projects (2021ZD0200500) awarded to X.Z. and Y.H. Y.H. was also supported by the National Science Foundation of Shanghai (23ZR1418400), and the Fundamental Research Funds for the Central Universities (2022ECNUXWK-XK003). This research has benefited from the financial support of IDEXLYON from Université de Lyon (project INDEPTH) within the Programme Investissements d'Avenir (ANR-16-IDEX-0005) and of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program Investissements d'Avenir (ANR-11-IDEX-007) operated by the French National Research Agency, as well as grants from the Agence Nationale pour la Recherche awarded to J.-C.D. (ANR-21-CE37-0032).

## COMPETING INTERESTS

The authors declare no conflicts of interest.

## DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

## ORCID

Shiwei Qiu ID https://orcid.org/0009-0001-8513-4328

Yancheng Tang ID https://orcid.org/0000-0002-4256-2525

Hongbo Yu ID https://orcid.org/0000-0002-3384-7772

Hanbo Xie ID https://orcid.org/0000-0003-1133-8544
Jean-Claude Dreher https://orcid.org/0000-0002-2157-1529

Yang Hu https://orcid.org/0000-0001-7659-5782

Xiaolin Zhou https://orcid.org/0000-0001-7363-4360

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**How to cite this article:** Qiu, S., Tang, Y., Yu, H., Xie, H., Dreher, J.-C., Hu, Y., & Zhou, X. (2025). Toward a computational understanding of bribe-taking behavior. *Ann NY Acad Sci.*, 1-11. https://doi.org/10.1111/nyas.15294