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on Cognitive and Behavioural Economics |
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Issue of 2026–02–02
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Liu He |
| Abstract: | Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.08247 |
| By: | Mayada Oudah; John Wooders |
| Abstract: | Facial expressions are central to human interaction, yet their role in strategic decision-making has received limited attention. We investigate how real-time facial communication influences cooperation in repeated social dilemmas. In a laboratory experiment, participants play a repeated Prisoner's Dilemma game under two conditions: in one, they observe their counterpart's facial expressions via gender-neutral avatars, and in the other no facial cues are available. Using state-of-the-art biometric technology to capture and display emotions in real-time, we find that facial communication significantly increases overall cooperation and, notably, promotes cooperation following defection. This restorative effect suggests that facial expressions help participants interpret defections less harshly, fostering forgiveness and the resumption of cooperation. While past actions remain the strongest predictor of behavior, our findings highlight the communicative power of facial expressions in shaping strategic outcomes. These results offer practical insights for designing emotionally responsive virtual agents and digital platforms that sustain cooperation in the absence of physical presence. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.15211 |
| By: | Tontrup, Stephan; Arlen, Jennifer; Sprigman, Christopher Jon |
| Abstract: | People often act prosocially and voluntarily conform to social and legal norms. This has fueled the idea that law can guide behavior through its expressive power. By contrast, we offer a theoretical and experimental framework suggesting that people strategically alter their decision-making environment to shift the norm applicable to their actions to one that is in their self-interest and to the detriment of others. Norm-shifting is one strategy within a broader concept we refer to as Behavioral Self-Management (BSM). To test norm-shifting, we implement a dictator game in which Allocators are offered an effort task before allocating a sum between themselves and a Recipient. Allocators receive the same endowment whether or not they work. We hypothesize that many will undertake the task to shift the applicable fairness norm from equal division to an effort-based norm that justifies their retaining a larger share. Prior evidence shows that costly effort is widely perceived as legitimizing unequal outcomes. We find that many Allocators decide to work, thereby reducing average transfers. Their work choices are strategic: their odds of working are higher the more they expect work to shift the fairness norm in their favor and the more prosocial they are-that is, the higher the moral costs they face for violating the fairness norm. Finally, Allocators who work make transfers that they expect to conform to an effort-based norm in the view of others, to maintain their self- and social-image. Our findings have implications for compliance with the law and with social norms. BSM can enable selfish non-compliance by undermining the social norms that underpin the law or by establishing social norms that provide justification for violation, while avoiding the social disapproval that would otherwise result. |
| Keywords: | Behavioral Self-management, Norm-shifting, Work, Self-and Social Image |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:335552 |
| By: | Fountain, John; McCosker, Michael; Morris, Dean |
| Abstract: | An experiment and operational subjective Bayesian statistical methods are used to investigate the relation between risk attitudes in the loss domain and framing effects. We find that subjects avoid pure increases in risk when such risks are transparent, that there is little or no correlation between risk attitudes in frames that alternately mask and make transparent pure increases in risk, and that analysing risk attitudes when prospects are presented as lists of prizes and probabilites overstates the likelihood of risk seeking in the loss domain. In general GEUT fails to predict better than a naive theory holding a uniform prior and Bayesian updating. The one exception is in a frame (viewed marginally) where costs of acquiring and processing information are low. |
| Keywords: | Risk and Uncertainty |
| URL: | https://d.repec.org/n?u=RePEc:ags:canzdp:263764 |