Do Not Revise Ockham's Razor Without Necessity

Ockham's razor asks that we not multiply entities beyond necessity. The razor is a powerful methodological tool, enabling us to articulate reasons for preferring one theory to another. There are those, however, who would modify the razor. Schaffer (2010: 313—our italics), for one, tells us that, ‘I think the proper rendering of Ockham's razor should be ‘Do not multiply fundamental entities without necessity’’. Our aim, here, is to challenge such re‐workings of Ockham's razor.     

Seeking Confirmation Is Rational for Deterministic Hypotheses

The tendency to test outcomes that are predicted by our current theory (the confirmation bias) is one of the best‐known biases of human decision making. We prove that the confirmation bias is an optimal strategy for testing hypotheses when those hypotheses are deterministic, each making a single prediction about the next event in a sequence. Our proof applies for two normative standards commonly used for evaluating hypothesis testing: maximizing expected information gain and maximizing the probability of falsifying the current hypothesis. This analysis rests on two assumptions: (a) that people predict the next event in a sequence in a way that is consistent with Bayesian inference; and (b) when testing hypotheses, people test the hypothesis to which they assign highest posterior probability. We present four behavioral experiments that support these assumptions, showing that a simple Bayesian model can capture people's predictions about numerical sequences (Experiments 1 and 2), and that we can alter the hypotheses that people choose to test by manipulating the prior probability of those hypotheses (Experiments 3 and 4).     

Inferring causal networks from observations and interventions

Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.     

Effects of explaining on children's preference for simpler hypotheses

Research suggests that the process of explaining influences causal reasoning by prompting learners to favor hypotheses that offer “good” explanations. One feature of a good explanation is its simplicity. Here, we investigate whether prompting children to generate explanations for observed effects increases the extent to which they favor causal hypotheses that offer simpler explanations, and whether this changes over the course of development. Children aged 4, 5, and 6 years observed several outcomes that could be explained by appeal to a common cause (the simple hypothesis) or two independent causes (the complex hypothesis). We varied whether children were prompted to explain each observation or, in a control condition, to report it. Children were then asked to make additional inferences for which the competing hypotheses generated different predictions. The results revealed developmental differences in the extent to which children favored simpler hypotheses as a basis for further inference in this task: 4-year-olds did not favor the simpler hypothesis in either condition; 5-year-olds favored the simpler hypothesis only when prompted to explain; and 6-year-olds favored the simpler hypothesis whether or not they explained.     

A simplification of the theory of simplicity

Nelson Goodman has constructed two theories of simplicity: one of predicates; one of hypotheses. I offer a simpler theory by generalization and abstraction from his. Generalization comes by dropping special conditions Goodman imposes on which unexcluded extensions count as complicating and which excluded extensions count as simplifying. Abstraction is achieved by counting only nonisomorphic models and subinterpretations. The new theory takes into account all the hypotheses of a theory in assessing its complexity, whether they were projected prior to, or result from, projection of a given hypothesis. It assigns simplicity post-projection priority over simplicity pre-projection. It better orders compound conditionals than does the theory of simplicity of hypotheses, and it does not inherit an anomaly of the theory of simplicity of predicates — its failure to order the ordering relations. Drop Goodman's special conditions, and the problems fall away with them.     

Procedural justice in authority relations: the strength of outcome dependence influences people's reactions to voice

In this article, we study how the strength of outcome dependence, defined as the extent to which people's outcomes depend on authority's decisions, influences their reactions to voice or no-voice procedures. We suggest that in situations where people are strongly outcome dependent they assume that the authority may not consider their views, and therefore voice procedures exert less influence on people's procedure judgments than in situations where they are not strongly outcome dependent. Findings of two experiments corroborate this line of reasoning: In strongly outcome dependent situations, recipients' procedure judgments are influenced less strongly by voice versus no-voice procedures than in moderate or weak outcome dependent situations. Furthermore, these effects were found for both pre-decision voice (Experiment 1) and for post-decision voice (Experiment 2). It is concluded that strong outcome dependence decreases the value-expressive function of voice opportunities. Copyright © 2007 John Wiley & Sons, Ltd.     

Can Multiple Biases Occur in a Single Situation? Evidence From Media Bias Research

This study explored multiple biases—the possibility that different biases would concurrently occur in a given situation, and each would exert its influence independently on people's judgments. The study focused on media bias through nonverbal (NV) behavior, where viewers judged an interviewed politician after they viewed the interview with a nonverbally friendly or hostile interviewer. In a meta‐analysis of several replications, 2 independent biases were found: media bias (viewers rated the interviewee more favorably when the interviewer's NV behavior was friendlier); and halo effect (viewers rated the interviewee according to the degree that they personally liked him). Regression analyses indicated that these 2 biases operated independently and additively on viewers' judgments. Implications for the study of multiple biases are discussed.     

The humble Bayesian: Model checking from a fully Bayesian perspective

Gelman and Shalizi (2012) criticize what they call the ‘usual story’ in Bayesian statistics: that the distribution over hypotheses or models is the sole means of statistical inference, thus excluding model checking and revision, and that inference is inductivist rather than deductivist. They present an alternative hypothetico‐deductive approach to remedy both shortcomings. We agree with Gelman and Shalizi's criticism of the usual story, but disagree on whether Bayesian confirmation theory should be abandoned. We advocate a humble Bayesian approach, in which Bayesian confirmation theory is the central inferential method. A humble Bayesian checks her models and critically assesses whether the Bayesian statistical inferences can reasonably be called upon to support real‐world inferences.     

The Accuracy of Intuitive Judgment Strategies: Covariation Assessment and Bayesian Inference

Most of people′s apparent strategies for covariation assessment and Bayesian inference can lead to errors. However, it is unclear how often and to what degree the strategies are inaccurate in natural contexts. Through Monte Carlo simulation, the respective normative and intuitive strategies for the two tasks were compared over many different situations. The results indicate that (a) under some general conditions, all the intuitive strategies perform much better than chance and many perform surprisingly well, and (b) some simple environmental variables have large effects on most of the intuitive strategies′ accuracy, not just in terms of the number of errors, but also in terms of the kinds of errors (e.g., incorrectly accepting versus incorrectly rejecting a hypothesis). Furthermore, common to many of the intuitive strategies is a disregard for the strength of the alternative hypothesis. Thus, a key to better performance in both tasks lies in considering alternative hypotheses, although this does not necessarily imply using a normative strategy (i.e., calculating the φ coefficient or using Bayes′ theorem). Some intuitive strategies take into account the alternative hypothesis and are accurate across environments. Because they are presumably simpler than normative strategies and are already part of people′s repertoire, using these intuitive strategies may be the most efficient means of ensuring highly accurate judgment in these tasks.     

What Do People Find Incompatible With Causal Determinism?

Four studies explored people's judgments about whether particular types of behavior are compatible with determinism. Participants read a passage describing a deterministic universe, in which everything that happens is fully caused by whatever happened before it. They then assessed the degree to which different behaviors were possible in such a universe. Other participants evaluated the extent to which each of these behaviors had various features (e.g., requiring reasoning). We assessed the extent to which these features predicted judgments about whether the behaviors were possible in a deterministic universe. Experiments 1 and 2 found that people's judgments about whether a behavior was compatible with determinism were not predicted by their judgments about whether that behavior relies on physical processes in the brain and body, is uniquely human, is unpredictable, or involves reasoning. Experiment 3, however, found that a distinction between what we call “active” and “passive” behaviors can explain people's judgments. Experiment 4 extended these findings, showing that we can measure this distinction in several ways and that it is robustly predicted by two different cues. Taken together, these results suggest that people carve up mentally guided behavior into two distinct types—understanding one type to be compatible with determinism, but another type to be fundamentally incompatible with determinism.     

Properties of the diversity effect in category-based inductive reasoning

Four experiments investigated how people judge the plausibility of category-based arguments, focusing on the diversity effect, in which arguments with diverse premise categories are considered particularly strong. In Experiment 1 we show that priming people as to the nature of the blank property determines whether sensitivity to diversity is observed. In Experiment 2 we find that people's hypotheses about the nature of the blank property predict judgements of argument strength. In Experiment 3 we examine the effect of our priming methodology on people's tendency to bring knowledge about causality or similarity to bear when evaluating arguments, and in Experiment 4 we show that whether people's hypotheses about the nature of the blank property were causal predicted ratings of argument strength. Together these results suggest that diversity effects occur because diverse premises lead people to bring general features of the premise categories to mind. Although our findings are broadly consistent with Bayesian and Relevance-based approaches to category-based inductive reasoning, neither approach captures all of our findings.     

Ampliative abduction

In Peirce's and Hanson's characterization of abductive inference, the abducted hypothesis (but not others) is present in the premises, so that the inference can hardly be taken as ampliative. Abduction has consequently been treated as part of the process whereby already generated hypotheses are judged in terms of their plausibility, simplicity, etc. I propose an interpretation of abduction which supports an ampliative view. It relies on a distinction between two logical stages in the generation of hypotheses, one “factual” and one “explanatory”. I also indicate how we may reconstruct Peirce's and Hanson's original inference in an ampliative form.     

Probabilistic Inferences Under Emotional Stress: How Arousal Affects Decision Processes

Many models of decision making neglect emotional states that could affect individuals' cognitive processes. The present work explores the effect of emotional stress on people's cognitive processes when making probabilistic inferences. Two contrasting hypotheses are tested against one another: the uncertainty‐reduction and attention‐narrowing hypotheses of how emotional stress affects decision making. In the experimental study, emotional stress was induced through the use of highly aversive pictures immediately before each decision. Emotional state was assessed by both subjective (state anxiety, arousal, and pleasantness ratings) and objective (skin conductance) measures. The results show that emotional stress impacts decision making; in particular, emotionally aroused participants seem to have focused on the most important information and selected simpler decision strategies relative to participants in a control condition. The results are in line with the attention‐narrowing hypothesis and suggest that emotional stress can impact decision making through limited predecisional information search and the selection of simpler decision strategies. Copyright © 2015 John Wiley & Sons, Ltd.     

Freedom,moral responsibility,and the failure of universal defeat

Proponents of manipulation arguments against compatibilism hold that manipulation scope (how many agents are manipulated) and manipulation type (whether the manipulator intends that an agent perform a particular action) do not impact judgments about free will and moral responsibility. Many opponents of manipulation arguments agree that manipulation scope has no impact but hold that manipulation type does. Recent work by Latham and Tierney (2022, 2023) found that people's judgments were sensitive to manipulation scope: people judged that an agent was less free and responsible when a manipulation was existential (impacting at least one but not all agents) than when the manipulation was universal (impacting every agent). This study examines people's judgements about existential and universal manipulation cases that involve both intentional and non-intentional outcomes. We found that manipulation scope also affects people's free will and responsibility judgments in manipulation cases involving both intentional and non-intentional outcomes. Interestingly, we also found that manipulation type influences the effect that manipulation scope has on people's free will judgments but not their moral responsibility judgments, which indicates that people's free will and responsibility judgments can come apart. This puts pressure on the prevalent assumption that judgments about free will and moral responsibility are conceptually bound together.     

WHAT LIARS CAN TELL US ABOUT THE KNOWLEDGE NORM OF PRACTICAL REASONING

If knowledge is the norm of practical reasoning, then we should be able to alter people's behavior by affecting their knowledge as well as by affecting their beliefs. Thus, as Roy Sorensen (2010 ) suggests, we should expect to find people telling lies that target knowledge rather than just lies that target beliefs. In this paper, however, I argue that Sorensen's discovery of “knowledge‐lies” does not support the claim that knowledge is the norm of practical reasoning. First, I use a Bayesian framework to show that in each of Sorensen's examples, knowledge‐lies alter people's behavior by affecting their beliefs. Second, I show that while we can imagine lies that target knowledge without targeting beliefs, they cannot alter people's behavior. In other words, knowledge‐lies actually work (i.e., manipulate behavior) by targeting beliefs or they do not work at all.     

Partisan Bias and Information Discounting in Economic Judgments

Research shows that partisanship biases people's views about the economy. Yet, there is little understanding of the factors, if any, that might mitigate the influence of partisanship on these judgments or the effect of partisanship on metacognitive judgments. This study uses an experimental design to show that partisanship continues to bias economic judgments even when subjects receive direct and neutral information about specific aspects of the economy. Moreover, it extends our understanding of partisan bias by showing it has a direct effect on people's metacognitive assessments of their own attitudes—particularly the degree of uncertainty people have in their own economic judgments. However, it appears that people are aware of the conflict between their partisan‐based judgment and economic information since we observe increases in economic uncertainty when information is counter to a subject's partisan predisposition. The results provide new insight into the extent of partisan bias and the difficulty of countering partisan‐based judgments.     

Why inconsistent leadership is regarded as procedurally unfair: the importance of social self‐esteem concerns

Three experimental studies examined to what extent leader's consistent use of procedures constitutes an important procedural fairness rule and influences people's reactions as a function of social self‐esteem. In line with a recent claim that more attention should be devoted to different procedural fairness rules (Brockner, Ackerman, & Fairchild, 2001 ), the findings of Study 1 demonstrated that inconsistent leaders were evaluated as less procedurally fair and influenced feelings of uncertainty about oneself in ongoing interpersonal interactions. Study 2 showed that manipulating leader's consistency influenced people's procedural fairness judgments and willingness to replace the leader, but only among those low in social self‐esteem (SSE). Finally, Study 3, using another consistency manipulation, demonstrated that variations in consistency made participants feel bad about themselves, particularly when they were low in SSE. These findings are discussed in light of research on relational models of justice and sociometer theory. Copyright © 2003 John Wiley & Sons, Ltd.     

A Process Model of Causal Reasoning

How do we make causal judgments? Many studies have demonstrated that people are capable causal reasoners, achieving success on tasks from reasoning to categorization to interventions. However, less is known about the mental processes used to achieve such sophisticated judgments. We propose a new process model—the mutation sampler—that models causal judgments as based on a sample of possible states of the causal system generated using the Metropolis–Hastings sampling algorithm. Across a diverse array of tasks and conditions encompassing over 1,700 participants, we found that our model provided a consistently closer fit to participant judgments than standard causal graphical models. In particular, we found that the biases introduced by mutation sampling accounted for people's consistent, predictable errors that the normative model by definition could not. Moreover, using a novel experimental methodology, we found that those biases appeared in the samples that participants explicitly judged to be representative of a causal system. We conclude by advocating sampling methods as plausible process-level accounts of the computations specified by the causal graphical model framework and highlight opportunities for future research to identify not just what reasoners compute when drawing causal inferences, but also how they compute it.     

Downsizing and Supersizing: How Changes in Product Attributes Influence Consumer Preferences

This paper investigates how changing the value of one attribute while keeping other attributes constant influences consumers' judgments and behaviors. We find that in two options, a proportionally equal change in one attribute tilts people's preference toward the option with higher (or lower) absolute magnitude of change when the change is desirable (or undesirable). We propose that when individuals face an attribute change, they use a deliberative and effortful response, known as System 2, to detect the change. However, they rely less on this system to evaluate the changed options. Instead, a more automatic System 1 processing influences their decision by making them apply the bigger‐is‐better heuristic (bigger‐is‐worse for an undesirable change) to prefer the option with the highest (lowest) absolute magnitude of change. Six studies demonstrate this phenomenon in both lab and real settings and support our hypothesis. Copyright © 2013 John Wiley & Sons, Ltd.