Nudges, regulations, and behavioral public choice.

Chater & Loewenstein have done a service to the field by raising the fundamental issue of how the political process distorts well-intentioned efforts at behavioral public policy. We connect this argument to broader research on government failure, particularly public choice theory in economics. We further suggest ways that behavioral research can help identify and mitigate such failures.

When do consumers favor overly precise information about investment returns?

Consumers are often shown investment returns with high levels of precision, which could lead them to misunderstand the inherent uncertainty. We test whether consumers are drawn to precision-that is offset the uncertainty in investment decisions by over-relying on precise numerical information. Five incentivized experiments compared decisions when expected growth is presented in precise forecasts as opposed to ranges. Consumers are more likely to prefer and invest more in precise forecasts when they are evaluated jointly with ranges and when the range features a potential loss. Under these circumstances, precise forecasts give consumers more confidence to invest. This effect holds when consumers are told investment returns are uncertain. On the other hand, experiencing discrepancies between expected and actual growth dissipates the preference for precise forecasts. We identify conditions under which consumers are more likely to favor precise forecasts and how this could be avoided if necessary. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Conviction Narrative Theory: A theory of choice under radical uncertainty.

Conviction Narrative Theory (CNT) is a theory of choice under radical uncertainty - situations where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas most theories of choice assume that people rely on (potentially biased) probabilistic judgments, such theories cannot account for adaptive decision-making when probabilities cannot be assigned. CNT proposes that people use narratives - structured representations of causal, temporal, analogical, and valence relationships - rather than probabilities, as the currency of thought that unifies our sense-making and decision-making faculties. According to CNT, narratives arise from the interplay between individual cognition and the social environment, with reasoners adopting a narrative that feels "right" to explain the available data; using that narrative to imagine plausible futures; and affectively evaluating those imagined futures to make a choice. Evidence from many areas of the cognitive, behavioral, and social sciences supports this basic model, including lab experiments, interview studies, and econometric analyses. We identify 12 propositions to explain how the mental representations (narratives) interact with four inter-related processes (explanation, simulation, affective evaluation, and communication), examining the theoretical and empirical basis for each. We conclude by discussing how CNT can provide a common vocabulary for researchers studying everyday choices across areas of the decision sciences.

Win-win denial: The psychological underpinnings of zero-sum thinking.

A core proposition in economics is that voluntary exchanges benefit both parties. We show that people often deny the mutually beneficial nature of exchange, instead espousing the belief that one or both parties fail to benefit from the exchange. Across four studies (and 8 further studies in the online supplementary materials), participants read about simple exchanges of goods and services, judging whether each party to the transaction was better off or worse off afterward. These studies revealed that win-win denial is pervasive, with buyers consistently seen as less likely to benefit from transactions than sellers. Several potential psychological mechanisms underlying win-win denial are considered, with the most important influences being mercantilist theories of value (confusing wealth for money) and theory of mind limits (failing to observe that people do not arbitrarily enter exchanges). We argue that these results have widespread implications for politics and society. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Negative consequences of failing to communicate uncertainties during a pandemic: an online randomised controlled trial on COVID-19 vaccines.

OBJECTIVE: To examine the impact of the government communicating uncertainties relating to COVID-19 vaccine effectiveness on vaccination intention and trust after people are exposed to conflicting information. DESIGN: Experimental design where participants were randomly allocated to one of two groups. SETTING: Online. PARTICIPANTS: 328 adults from a UK research panel. INTERVENTION: Participants received either certain or uncertain communications from a government representative about COVID-19 vaccine effectiveness, before receiving conflicting information about effectiveness. MAIN OUTCOME MEASURES: Vaccination intention and trust in government. RESULTS: Compared with those who received the uncertain announcement from the government, participants who received the certain announcement reported a greater loss of vaccination intention (d=0.34, 95% CI (0.12 to 0.56), p=0.002) and trust (d=0.34, 95% CI (0.12 to 0.56), p=0.002) after receiving conflicting information. CONCLUSIONS: Communicating with certainty about COVID-19 vaccines reduces vaccination intention and trust if conflicting information arises, whereas communicating uncertainties can protect people from the negative impact of exposure to conflicting information. There are likely to be other factors affecting vaccine intentions, which we do not account for in this study. TRIAL REGISTRATION NUMBER: Open Science Framework: https://osf.io/c73px/.

Principles of moral accounting: How our intuitive moral sense balances rights and wrongs.

We are all saints and sinners: Some of our actions benefit others, while other actions lead to harm. How do people balance moral rights against moral wrongs when evaluating others' actions? Across 9 studies, we contrast the predictions of three conceptions of intuitive morality-outcome-based (utilitarian), act-based (deontologist), and person-based (virtue ethics) approaches. These experiments establish four principles: Partial offsetting (good acts can partly offset bad acts), diminishing sensitivity (the extent of the good act has minimal impact on its offsetting power), temporal asymmetry (good acts are more praiseworthy when they come after harms), and act congruency (good acts are more praiseworthy to the extent they offset a similar harm). These principles are difficult to square with utilitarian or deontological approaches, but sit well within person-based approaches to moral psychology. Inferences about personal character mediated many of these effects (Studies 1-4), explained differences across items and across individuals (Studies 5-6), and could be manipulated to produce downstream consequences on blame (Studies 7-9); however, there was some evidence for more modest roles of utilitarian and deontological processing too. These findings contribute to conversations about moral psychology and person perception, and may have policy and marketing implications.

Belief digitization: Do we treat uncertainty as probabilities or as bits?

Humans are often characterized as Bayesian reasoners. Here, we question the core Bayesian assumption that probabilities reflect degrees of belief. Across eight studies, we find that people instead reason in a digital manner, assuming that uncertain information is either true or false when using that information to make further inferences. Participants learned about 2 hypotheses, both consistent with some information but one more plausible than the other. Although people explicitly acknowledged that the less-plausible hypothesis had positive probability, they ignored this hypothesis when using the hypotheses to make predictions. This was true across several ways of manipulating plausibility (simplicity, evidence fit, explicit probabilities) and a diverse array of task variations. Taken together, the evidence suggests that digitization occurs in prediction because it circumvents processing bottlenecks surrounding people's ability to simulate outcomes in hypothetical worlds. These findings have implications for philosophy of science and for the organization of the mind. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Simplicity and complexity preferences in causal explanation: An opponent heuristic account.

People often prefer simple to complex explanations because they generally have higher prior probability. However, simpler explanations are not always normatively superior because they often do not account for the data as well as complex explanations. How do people negotiate this trade-off between prior probability (favoring simple explanations) and goodness-of-fit (favoring complex explanations)? Here, we argue that people use opponent heuristics to simplify this problem-that people use simplicity as a cue to prior probability but complexity as a cue to goodness-of-fit. Study 1 finds direct evidence for this claim. In subsequent studies, we examine factors that lead one or the other heuristic to predominate in a given context. Studies 2 and 3 find that people have a stronger simplicity preference in deterministic rather than stochastic contexts, while Studies 4 and 5 find that people have a stronger simplicity preference for physical rather than social causal systems, suggesting that people use abstract expectations about causal texture to modulate their explanatory inferences. Together, we argue that these cues and contextual moderators act as powerful constraints that can help to specify the otherwise ill-defined problem of what distributions to use in Bayesian hypothesis comparison.

Intuitions about mathematical beauty: A case study in the aesthetic experience of ideas.

Can an idea be beautiful? Mathematicians often describe arguments as "beautiful" or "dull," and famous scientists have claimed that mathematical beauty is a guide toward the truth. Do laypeople, like mathematicians and scientists, experience mathematics aesthetically? Three studies suggest that they do. When people rated the similarity of simple mathematical arguments to landscape paintings (Study 1) or pieces of classical piano music (Study 2), their similarity rankings were internally consistent across participants. Moreover, when participants rated beauty and various other potentially aesthetic dimensions for artworks and mathematical arguments, they relied mainly on the same three dimensions for judging beauty-elegance, profundity, and clarity (Study 3). These aesthetic judgments, made separately for artworks and arguments, could be used to predict similarity judgments out-of-sample. These studies also suggest a role for expertise in sharpening aesthetic intuitions about mathematics. We argue that these results shed light on broader issues in how and why humans have aesthetic experiences of abstract ideas.

Financial alchemists and financial shamans.

Professional money management appears to require little skill, yet its practitioners command astronomical salaries. Singh's theory of shamanism provides one possible explanation: Financial professionals are the shamans of the global economy. They cultivate the perception of superhuman traits, maintain grueling initiation rituals, and rely on esoteric divination rituals. An anthropological view of markets can usefully supplement economic and psychological approaches.

Why do people believe in a zero-sum economy?

Zero-sum thinking and aversion to trade pervade our society, yet fly in the face of everyday experience and the consensus of economists. Boyer & Petersen's (B&P's) evolutionary model invokes coalitional psychology to explain these puzzling intuitions. I raise several empirical challenges to this explanation, proposing two alternative mechanisms - intuitive mercantilism (assigning value to money rather than goods) and errors in perspective-taking.

Preferences for Explanation Generality Develop Early in Biology But Not Physics.

One of the core functions of explanation is to support prediction and generalization. However, some explanations license a broader range of predictions than others. For instance, an explanation about biology could be presented as applying to a specific case (e.g., "this bear") or more generally across "all animals." The current study investigated how 5- to 7-year-olds (N = 36), 11- to 13-year-olds (N = 34), and adults (N = 79) evaluate explanations at varying levels of generality in biology and physics. Findings revealed that even the youngest children preferred general explanations in biology. However, only older children and adults preferred explanation generality in physics. Findings are discussed in light of differences in our intuitions about biological and physical principles.

The effect of abstract versus concrete framing on judgments of biological and psychological bases of behavior.

Human behavior is frequently described both in abstract, general terms and in concrete, specific terms. We asked whether these two ways of framing equivalent behaviors shift the inferences people make about the biological and psychological bases of those behaviors. In five experiments, we manipulated whether behaviors are presented concretely (i.e. with reference to a specific person, instantiated in the particular context of that person's life) or abstractly (i.e. with reference to a category of people or behaviors across generalized contexts). People judged concretely framed behaviors to be less biologically based and, on some dimensions, more psychologically based than the same behaviors framed in the abstract. These findings held true for both mental disorders (Experiments 1 and 2) and everyday behaviors (Experiments 4 and 5), and yielded downstream consequences for the perceived efficacy of disorder treatments (Experiment 3). Implications for science educators, students of science, and members of the lay public are discussed.

Little Bayesians or little Einsteins? Probability and explanatory virtue in children's inferences.

Like scientists, children seek ways to explain causal systems in the world. But are children scientists in the strict Bayesian tradition of maximizing posterior probability? Or do they attend to other explanatory considerations, as laypeople and scientists - such as Einstein - do? Four experiments support the latter possibility. In particular, we demonstrate in four experiments that 4- to 8-year-old children, like adults, have a robust latent scope bias that leads to inferences that do not maximize posterior probability. When faced with two explanations equally consistent with observed data, where one explanation makes an unverified prediction, children consistently preferred the explanation that does not make this prediction (Experiment 1), even if the prior probabilities are identical (Experiment 3). Additional evidence suggests that this latent scope bias may result from the same explanatory strategies used by adults (Experiments 1 and 2), and can be attenuated by strong prior odds (Experiment 4). We argue that children, like adults, rely on 'explanatory virtues' in inference - a strategy that often leads to normative responses, but can also lead to systematic error.

Sense-making under ignorance.

Much of cognition allows us to make sense of things by explaining observable evidence in terms of unobservable explanations, such as category memberships and hidden causes. Yet we must often make such explanatory inferences with incomplete evidence, where we are ignorant about some relevant facts or diagnostic features. In seven experiments, we studied how people make explanatory inferences under these uncertain conditions, testing the possibility that people attempt to infer the presence or absence of diagnostic evidence on the basis of other cues such as evidence base rates (even when these cues are normatively irrelevant) and then proceed to make explanatory inferences on the basis of the inferred evidence. Participants followed this strategy in both diagnostic causal reasoning (Experiments 1-4, 7) and in categorization (Experiments 5-6), leading to illusory inferences. Two processing predictions of this account were also confirmed, concerning participants' evidence-seeking behavior (Experiment 4) and their beliefs about the likely presence or absence of the evidence (Experiment 5). These findings reveal deep commonalities between superficially distinct forms of diagnostic reasoning-causal reasoning and classification-and point toward common inferential machinery across explanatory tasks.

The influence of framing on clinicians' judgments of the biological basis of behaviors.

Practicing clinicians frequently think about behaviors both abstractly (i.e., in terms of symptoms, as in the Diagnostic and Statistical Manual of Mental Disorders, 5th ed., DSM-5; American Psychiatric Association, 2013) and concretely (i.e., in terms of individual clients, as in DSM-5 Clinical Cases; Barnhill, 2013). Does abstract/concrete framing influence clinical judgments about behaviors? Practicing mental health clinicians (N = 74) were presented with hallmark symptoms of 6 disorders framed abstractly versus concretely, and provided ratings of their biological and psychological bases (Experiment 1) and the likely effectiveness of medication and psychotherapy in alleviating them (Experiment 2). Clinicians perceived behavioral symptoms in the abstract to be more biologically and less psychologically based than when concretely described, and medication was viewed as more effective for abstractly than concretely described symptoms. These findings suggest a possible basis for miscommunication and misalignment of views between primarily research-oriented and primarily practice-oriented clinicians; furthermore, clinicians may accept new neuroscience research more strongly in the abstract than for individual clients.

Do the right thing: the assumption of optimality in lay decision theory and causal judgment.

Human decision-making is often characterized as irrational and suboptimal. Here we ask whether people nonetheless assume optimal choices from other decision-makers: Are people intuitive classical economists? In seven experiments, we show that an agent's perceived optimality in choice affects attributions of responsibility and causation for the outcomes of their actions. We use this paradigm to examine several issues in lay decision theory, including how responsibility judgments depend on the efficacy of the agent's actual and counterfactual choices (Experiments 1-3), individual differences in responsibility assignment strategies (Experiment 4), and how people conceptualize decisions involving trade-offs among multiple goals (Experiments 5-6). We also find similar results using everyday decision problems (Experiment 7). Taken together, these experiments show that attributions of responsibility depend not only on what decision-makers do, but also on the quality of the options they choose not to take.

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment.

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge­an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms­causal islands­such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1-3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5).

Causal inference and the hierarchical structure of experience.

Children and adults make rich causal inferences about the physical and social world, even in novel situations where they cannot rely on prior knowledge of causal mechanisms. We propose that this capacity is supported in part by constraints provided by event structure--the cognitive organization of experience into discrete events that are hierarchically organized. These event-structured causal inferences are guided by a level-matching principle, with events conceptualized at one level of an event hierarchy causally matched to other events at that same level, and a boundary-blocking principle, with events causally matched to other events that are parts of the same superordinate event. These principles are used to constrain inferences about plausible causal candidates in unfamiliar situations, both in diagnosing causes (Experiment 1) and predicting effects (Experiment 2). The results could not be explained by construal level (Experiment 3) or similarity-matching (Experiment 4), and were robust across a variety of physical and social causal systems. Taken together, these experiments demonstrate a novel way in which noncausal information we extract from the environment can help to constrain inferences about causal structure.