Behavioral science should start by assuming people are reasonable.

Should policymaking assume humans are irrational? Using empirical, theoretical, and philosophical arguments, we suggest a more useful frame is that human behavior is reasonable. Through identifying goals and systemic factors shaping behavior, we suggest that assuming people are reasonable enables behavioral science to be more effective in shaping public policy.

Psychological inoculation can reduce susceptibility to misinformation in large rational agent networks.

The unchecked spread of misinformation is recognized as an increasing threat to public, scientific and democratic health. Online networks are a contributing cause of this spread, with echo chambers and polarization indicative of the interplay between the search behaviours of users and reinforcement processes within the system they inhabit. Recent empirical work has focused on interventions aimed at inoculating people against misinformation, yielding success on the individual level. However, given the evolving, dynamic information context of online networks, important questions remain regarding how such inoculation interventions interact with network systems. Here we use an agent-based model of a social network populated with belief-updating users. We find that although equally rational agents may be assisted by inoculation interventions to reject misinformation, even among such agents, intervention efficacy is temporally sensitive. We find that as beliefs disseminate, users form self-reinforcing echo chambers, leading to belief consolidation-irrespective of their veracity. Interrupting this process requires 'front-loading' of inoculation interventions by targeting critical thresholds of network users before consolidation occurs. We further demonstrate the value of harnessing tipping point dynamics for herd immunity effects, and note that inoculation processes do not necessarily lead to increased rates of 'false-positive' rejections of truthful communications.

The impact of partial source dependence on belief and reliability revision.

In this article, we explore how people revise their belief in a hypothesis and the reliability of sources in circumstances where those sources are either independent or are partially dependent because of their shared, common background. Specifically, we examine people's revision of perceived source reliability by comparison with a formal model of reliability revision proposed by Bovens and Hartmann (2003). This model predicts a U-shaped trajectory for revision in certain circumstances: If a source provides a positive report for an unlikely hypothesis, perceived source reliability should decrease; as additional positive reports emerge, however, estimates of reliability should increase. Participants' updates in our experiment show this U-shaped pattern. Furthermore, participants' responses also respect a second feature of the model, namely that perceived reliability should once again decrease when it becomes known that the sources are partially dependent. Participants revise appropriately both when a specific shared reliability is observed (e.g., sources went to the same, low quality school) and when integrating the possibility of shared reliability. These findings shed light on how people gauge source reliability and integrate reports when multiple sources weigh in on an issue as seen in public debates. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Large networks of rational agents form persistent echo chambers.

Echo chambers (ECs) are enclosed epistemic circles where like-minded people communicate and reinforce pre-existing beliefs. It remains unclear if cognitive errors are necessarily required for ECs to emerge, and then how ECs are able to persist in networks with available contrary information. We show that ECs can theoretically emerge amongst error-free Bayesian agents, and that larger networks encourage rather than ameliorate EC growth. This suggests that the network structure itself contributes to echo chamber formation. While cognitive and social biases might exacerbate EC emergence, they are not necessary conditions. In line with this, we test stylized interventions to reduce EC formation, finding that system-wide truthful 'educational' broadcasts ameliorate the effect, but do not remove it entirely. Such interventions are shown to be more effective on agents newer to the network. Critically, this work serves as a formal argument for the responsibility of system architects in mitigating EC formation and retention.

A method for evaluating cognitively informed micro-targeted campaign strategies: An agent-based model proof of principle.

In political campaigns, perceived candidate credibility influences the persuasiveness of messages. In campaigns aiming to influence people's beliefs, micro-targeted campaigns (MTCs) that target specific voters using their psychological profile have become increasingly prevalent. It remains open how effective MTCs are, notably in comparison to population-targeted campaign strategies. Using an agent-based model, the paper applies recent insights from cognitive models of persuasion, extending them to the societal level in a novel framework for exploring political campaigning. The paper provides an initial treatment of the complex dynamics of population level political campaigning in a psychologically informed manner. Model simulations show that MTCs can take advantage of the psychology of the electorate by targeting voters favourable disposed towards the candidate. Relative to broad campaigning, MTCs allow for efficient and adaptive management of complex campaigns. Findings show that disliked MTC candidates can beat liked population-targeting candidates, pointing to societal questions concerning campaign regulations.