Rational vigilance of intentions and incentives guides learning from advice

Sorting Fact from Fiction When Reasoning Is Motivated

We combine a simple theoretical framework with a survey experiment to investigate how individuals sort fact from fiction and update their assessments in societally important but politicized topics, such as climate change and public health. We show that cognitive ability improves news discernment and in particular, the ability to make correct assessments that contradict one’s prior issue positions. However, when we disaggregate results by topic, higher cognitive ability sometimes amplifies motivated decision-making rather than attenuating it. Our framework and findings suggest a hard–easy pattern: more complex issues elicit greater motivated reasoning; simpler ones elicit less. In addition, overconfidence reduces responsiveness to new information, contributing to the persistence of misperceptions. Higher institutional trust appears to weaken motivated reasoning, suggesting that institutional quality may play an important role in constraining opinion polarization.

External incentives change the relation between confidence and epistemic curiosity

Behavioral Intervention Construal: A Framework for Understanding Inferences from Behavioral Interventions

Organizations frequently use behavioral interventions—including incentives, messaging campaigns, and modifications to choice architecture—to influence people’s behavior. However, recent evidence suggests that such interventions often have inconsistent effects across implementations. To understand this variability, we start from the premise that interventions are not simply levers for changing behavior; they also signal information to decision-makers. For example, when an option is set as the default, people may infer that it is recommended by the organization—an inference that can increase the likelihood they stick with that option. But when defaults appear self-serving, people may reject them. Here, we propose a framework to better understand the effects of behavioral interventions by identifying the inferences people draw when encountering them. In developing this framework, we first characterize the defining features of behavioral interventions that determine how they are psychologically experienced. These features implicate five fundamental needs, which guide decision-makers’ information-seeking processes. From these needs we derive a principled typology of inferences drawn by decision-makers in response to behavioral interventions, bringing together scattered evidence for these inferences from across the behavioral sciences. The resulting framework entails predictions about when specific inferences will arise and how these inferences influence the overall effects of behavioral interventions. Utilizing the framework, we provide a tool for enhancing intervention design. Together, the current research offers an integrative framework for advancing theory on behavioral interventions while also outlining actionable insights for organizations hoping to promote behavior change at scale.

When Direct Prediction Fails: Evidence from LLM-Based Misinformation Risk Evaluation

The results suggest that directly asking for the target response may not always yield the most effective score for predicting it, and that comparing direct scores with indirect paths through related judgments may reveal a more effective predictive route.

Not All Explanations Are Sought: Information-Seeking Psychology for Human-Centered XAI

This position paper argues that human-centered explainable AI (HCXAI) should incorporate insights from the psychology of information seeking. Drawing on Sharot and Sunstein's framework of information-seeking motives, we propose that people evaluate whether to engage with explanations based on three types of expected utility: instrumental (will it help me act better?), hedonic (will it make me feel better?), and cognitive (will it improve my understanding?). Each utility is estimated through a lens shaped by well-documented cognitive biases, including illusion of control, automation bias, unrealistic optimism, impact bias, overconfidence, and confirmation bias. These biases can lead to two failure modes: excessive information-seeking that fragments attention without improving decisions, and insufficient information-seeking that leaves critical risks and misunderstandings unexamined. This challenge is particularly acute for agentic AI systems, where explanations must support not just understanding a single output but anticipating cascading actions, assessing risks, and deciding when to intervene. By integrating information-seeking psychology into HCXAI, we advocate for a shift from making explanations available to making them sought: designing systems that account for when and why users actually want to know.

Leveraging Collective Advice-Taking Behavior to Infer Accuracy and Improve the Wisdom of Crowds

Wisdom of crowds estimates can be compromised when some agents’ predictions are systematically biased. A natural remedy is to aggregate predictions from a subset of more accurate agents rather than the entire crowd. I propose cluster weight on advice (CWOA), a novel “two-shot” algorithm to identify a more accurate subgroup in a single-prediction-problem context. CWOA first applies kernel density estimation to identify clusters of similar initial predictions. If multiple clusters emerge—indicating potential heterogeneity in agents’ information—the algorithm proceeds to present a piece of numerical advice (e.g., the group mean) and elicit updated predictions. This enables the calculation of the weight on advice (WOA)—the scaled magnitude of each agent’s belief revision. CWOA then averages the updated predictions within the cluster with the lowest mean WOA. A behavioral model and simulations explain both why and when cluster-level WOA signals accuracy. Better-informed agents—having already incorporated higher-quality information—perceive less corrective value in the advice and therefore, exhibit lower WOA. CWOA does not require agents to know the true biases or the composition of the crowd; a modest relative advantage in perceived estimation bias by better-informed agents may be sufficient, even under misperceptions of variance, advice quality, and psychological biases in advice taking. Empirically, I first test and confirm the model’s key insight in a controlled experimental setting. I then validate CWOA’s performance across multiple preregistered and archival data sets, including a study in which numerical advice comes from artificial intelligence. CWOA consistently outperforms benchmarks, including the state-of-the-art metaprediction-based methods. This paper was accepted by Jack Soll, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04499 .