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.

Strange but true: Corroboration and base rate neglect.

How do we deal with unlikely witness testimonies? Whether in legal or everyday reasoning, corroborative evidence is generally considered a strong marker of support for the reported hypothesis. However, questions remain regarding how the prior probability, or base rate, of that hypothesis interacts with corroboration. Using a Bayesian network model, we illustrate an inverse relationship between the base rate of a hypothesis, and the support provided by corroboration. More precisely, as the base rate of hypothesis becomes more unlikely (and thus there is lower expectation of corroborating testimony), each piece of confirming testimony provides a nonlinear increase in support, relative to a more commonplace hypothesis-assuming independence between witnesses. We show across 3 experiments that lay reasoners consistently fail to account for this impact of (rare) base rates in both diagnostic and intercausal reasoning, resulting in substantial underestimation in belief updating. We consider this a novel demonstration of an inverted form of base rate neglect. We highlight the implications of this work for any scenario in which one cannot assume the confirmation or disconfirmation of a reported hypothesis is uniform. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The rational continued influence of misinformation.

Misinformation has become an increasingly topical field of research. Studies on the 'Continued Influence Effect' (CIE) show that misinformation continues to influence reasoning despite subsequent retraction. Current explanatory theories of the CIE tacitly assume continued reliance on misinformation is the consequence of a biased process. In the present work, we show why this perspective may be erroneous. Using a Bayesian formalism, we conceptualize the CIE as a scenario involving contradictory testimonies and incorporate the previously overlooked factors of the temporal dependence (misinformation precedes its retraction) between, and the perceived reliability of, misinforming and retracting sources. When considering such factors, we show the CIE to have normative backing. We demonstrate that, on aggregate, lay reasoners (N = 101) intuitively endorse the necessary assumptions that demarcate CIE as a rational process, still exhibit the standard effect, and appropriately penalize the reliability of contradicting sources. Individual-level analyses revealed that although many participants endorsed assumptions for a rational CIE, very few were able to execute the complex model update that the Bayesian model entails. In sum, we provide a novel illustration of the pervasive influence of misinformation as the consequence of a rational process.

Strategies for selecting and evaluating information.

Within the domain of psychology, Optimal Experimental Design (OED) principles have been used to model how people seek and evaluate information. Despite proving valuable as computational-level methods to account for people's behaviour, their descriptive and explanatory powers remain largely unexplored. In a series of experiments, we used a naturalistic crime investigation scenario to examine how people evaluate queries, as well as outcomes, in probabilistic contexts. We aimed to uncover the psychological strategies that people use, not just to assess whether they deviated from OED principles. In addition, we explored the adaptiveness of the identified strategies across both one-shot and stepwise information search tasks. We found that people do not always evaluate queries strictly in OED terms and use distinct strategies, such as by identifying a leading contender at the outset. Moreover, we identified aspects of zero-sum thinking and risk aversion that interact with people's information search strategies. Our findings have implications for building a descriptive account of information seeking and evaluation, accounting for factors that currently lie outside the realm of information-theoretic OED measures, such as context and the learner's own preferences.

Evaluating scenarios toward zero plastic pollution.

Plastic pollution is a pervasive and growing problem. To estimate the effectiveness of interventions to reduce plastic pollution, we modeled stocks and flows of municipal solid waste and four sources of microplastics through the global plastic system for five scenarios between 2016 and 2040. Implementing all feasible interventions reduced plastic pollution by 40% from 2016 rates and 78% relative to "business as usual" in 2040. Even with immediate and concerted action, 710 million metric tons of plastic waste cumulatively entered aquatic and terrestrial ecosystems. To avoid a massive build-up of plastic in the environment, coordinated global action is urgently needed to reduce plastic consumption; increase rates of reuse, waste collection, and recycling; expand safe disposal systems; and accelerate innovation in the plastic value chain.

Dependencies in evidential reports: The case for informational advantages.

Whether assessing the accuracy of expert forecasting, the pros and cons of group communication, or the value of evidence in diagnostic or predictive reasoning, dependencies between experts, group members, or evidence have traditionally been seen as a form of redundancy. We demonstrate that this conception of dependence conflates the structure of a dependency network, and the observations across this network. By disentangling these two elements we show, via mathematical proof and specific examples, that there are cases where dependencies yield an informational advantage over independence. More precisely, when a structural dependency exists, but observations are either partial or contradicting, these observations provide more support to a hypothesis than when this structural dependency does not exist, ceteris paribus. Furthermore, we show that lay reasoners endorse sufficient assumptions underpinning these advantageous structures yet fail to appreciate their implications for probability judgments and belief revision.

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).

Widening Access to Bayesian Problem Solving.

Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic training on probabilistic reasoning. We discuss the implications of this finding for the use of Bayesian network software tools in applied contexts such as security, medical, forensic, economic or environmental decision making.

False prophets and Cassandra's curse: The role of credibility in belief updating.

Information from other sources can be beneficial or detrimental, depending on the veracity of the report. Along with prior beliefs and context, recipients have two main routes to determine veracity; the perceived credibility of the source and direct-evaluation via first-hand evidence, i.e. testing the advice against observation. Using a probabilistic learning paradigm, we look at the interplay of these two factors in the uptake (or rejection) of communicated beliefs, and the subsequent evaluation of the credibility of the communicator in light of this process. Whether the communicated belief is false (Experiment 1), or true (Experiment 2), we show that beliefs are interpreted in light of the perceived credibility of the source, such that beliefs from high trust sources are taken up (hypothesis 1), whilst beliefs from low trust sources are treated with suspicion and potentially rejected - dependent on early evidence experiences (hypothesis 2). Finally, we show that these credibility-led biased interpretations of evidence (whether belief or suspicion confirming) lead to further polarization of the perceived credibility of communicators (hypothesis 3). Crucially, this occurs irrespective of the veracity of the communication, such that sources accompanied by a high trust cue not only get away with communicating falsehoods, but see their perceived credibility increase, whilst sources accompanied by low trust cues not only have truthful communications rejected, but have their low trust penalized even further. These findings carry important implications for the consequences of artificially inflating or deflating the credibility of communicators (e.g., politicians or scientists in public debate).

Influence and seepage: An evidence-resistant minority can affect public opinion and scientific belief formation.

Some well-established scientific findings may be rejected by vocal minorities because the evidence is in conflict with political views or economic interests. For example, the tobacco industry denied the medical consensus on the harms of smoking for decades, and the clear evidence about human-caused climate change is currently being rejected by many politicians and think tanks that oppose regulatory action. We present an agent-based model of the processes by which denial of climate change can occur, how opinions that run counter to the evidence can affect the scientific community, and how denial can alter the public discourse. The model involves an ensemble of Bayesian agents, representing the scientific community, that are presented with the emerging historical evidence of climate change and that also communicate the evidence to each other. Over time, the scientific community comes to agreement that the climate is changing. When a minority of agents is introduced that is resistant to the evidence, but that enter into the scientific discussion, the simulated scientific community still acquires firm knowledge but consensus formation is delayed. When both types of agents are communicating with the general public, the public remains ambivalent about the reality of climate change. The model captures essential aspects of the actual evolution of scientific and public opinion during the last 4 decades.

The Zero-Sum Fallacy in Evidence Evaluation.

There are many instances, both in professional domains such as law, forensics, and medicine and in everyday life, in which an effect (e.g., a piece of evidence or event) has multiple possible causes. In three experiments, we demonstrated that individuals erroneously assume that evidence that is equally predicted by two competing hypotheses offers no support for either hypothesis. However, this assumption holds only in cases in which competing causes are mutually exclusive and exhaustive (i.e., exactly one cause is true). We argue that this reasoning error is due to a zero-sum perspective on evidence, wherein people assume that evidence that supports one causal hypothesis must disconfirm its competitor. Thus, evidence cannot give positive support to both competitors. Across three experiments ( N = 49, N = 193, N = 201), we demonstrated that this error is robust to intervention and generalizes across several different contexts. We also ruled out several alternative explanations of the bias.

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.

Communicated beliefs about action-outcomes: The role of initial confirmation in the adoption and maintenance of unsupported beliefs.

As agents seeking to learn how to successfully navigate their environments, humans can both obtain knowledge through direct experience, and second-hand through communicated beliefs. Questions remain concerning how communicated belief (or instruction) interacts with first-hand evidence integration, and how the former can bias the latter. Previous research has revealed that people are more inclined to seek out confirming evidence when they are motivated to uphold the belief, resulting in confirmation bias. The current research explores whether merely communicated beliefs affect evidence integration over time when it is not of interest to uphold the belief, and all evidence is readily available. In a novel series of on-line experiments, participants chose on each trial which of two options to play for money, being exposed to outcomes of both. Prior to this, they were exposed to favourable communicated beliefs regarding one of two options. Beliefs were either initially supported or undermined by subsequent probabilistic evidence (probabilities reversed halfway through the task, rendering the options equally profitable overall). Results showed that while communicated beliefs predicted initial choices, they only biased subsequent choices when supported by initial evidence in the first phase of the experiment. Findings were replicated across contexts, evidence sequence lengths, and probabilistic distributions. This suggests that merely communicated beliefs can prevail even when not supported by long run evidence, and in the absence of a motivation to uphold them. The implications of the interaction between communicated beliefs and initial evidence for areas including instruction effects, impression formation, and placebo effects are discussed.