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on Neuroeconomics |
| By: | Daniel Banko-Ferran (University of Pittsburgh); Valeria Burdea (LMU Munich); Jonathan Woon (University of Pittsburgh) |
| Abstract: | This study evaluates the effectiveness of three widely used belief elicitation methods in an online setting: the binarized scoring rule (BSR), the stochastic Becker-DeGroot-Marschak mechanism (BDM), and unincentivized introspection. Despite the theoretical advantages of incentive-compatible methods (BSR and BDM), we find that they impose significantly higher cognitive costs on participants, requiring more time and effort to implement, without delivering clear improvements in belief accuracy. In fact, BSR systematically leads to greater errors in reported beliefs compared to introspection, while BDM also reduces accuracy, though to a lesser extent. Surprisingly, individual differences in probabilistic reasoning skills do not mitigate these errors for BSR but do help improve accuracy under BDM. Our findings suggest that simpler, unincentivized approaches may offer comparable or even superior accuracy at a lower cognitive cost. These results have broad implications for the design of experiments and the interpretation of belief data in behavioral and experimental economics. |
| Keywords: | belief elicitation; induced beliefs; incentives; online experiment; |
| JEL: | C81 C89 D83 D91 |
| Date: | 2026–01–20 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:562 |
| 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: | Dipankar Das |
| Abstract: | This paper presents the Sequential Rationality Hypothesis, which argues that consumers are better able to make utility-maximizing decisions when products appear in sequential pairwise comparisons rather than in simultaneous multi-option displays. Although this involves higher cognitive costs than the all-at-once format, the current digital market, with its diverse products listed by review ratings, pricing, and paid products, often creates inconsistent choices. The present work shows that preparing the list sequentially supports more rational choice, as the consumer tries to minimize cognitive costs and may otherwise make an irrational decision. If the decision remains the same on both offers, then that is a consistent preference. The platform uses this approach by reducing cognitive costs while still providing the list in an all-at-once format rather than sequentially. To show how sequential exposure reduces cognitive overload and prevents context-dependent errors, we develop a bounded attention model and extend the monotonic attention rule of the random attention model to theorize the sequential rational hypothesis. Using a theoretical design with common consumer goods, we test these hypotheses. This theoretical model helps policymakers in digital market laws, behavioral economics, marketing, and digital platform design consider how choice architectures may improve consumer choices and encourage rational decision-making. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.15332 |
| By: | Jingxuan, Sun; Changhao, Li; He, Leng; Sheetal, Abhishek (The Hong Kong Polytechnic University) |
| Abstract: | The social competition has become increasingly fierce, and individuals’ perception of success is guided by cooperation and shared benefits. Some people define success as a scarce and exclusive resource, which is referred to as the “zero-sum construal of success”. This viewpoint will weaken individuals’ tendency to actively help others and their willingness to share knowledge. This study aims to evaluate whether the zero-sum construal of success has a predictable structural basis shaped by macroeconomic and social-status factors. We based our research on replicating the research of Sirola and Pitesa (2017), using Bayesian regression and XGBoost models introduced to empirically assess the predictability of the zero-sum construal of success. We find little evidence supporting the replicability of the original findings. The research results indicate that zero-sum belief is not a randomly formed attitude tendency but a psychological cognitive structure constructed by socioeconomic factors. It also shows that economic pressure affects individuals at the emotional level, and it shapes competitive cognitive paradigms through learnable social mechanisms. The findings of this study provide a new research perspective for understanding the relationship between economic cycle fluctuations and social psychological changes. |
| Date: | 2026–01–03 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:vzysu_v1 |