In lieu of an abstract, here is a brief excerpt of the content:Philosophy and Rhetoric 34.2 (2001) 93-112 [Access article in PDF] Enthymemes, Common Knowledge, and Plausible Inference Douglas Walton The study of enthymemes has always been regarded as important in logic, critical thinking, and rhetoric, but too often it is the formal or mechanistic aspect of it that has been in the forefront. This investigation will show that there is a kind of plausibilistic script-based reasoning, of a (...) kind that has mainly been studied in artificial intelligence, that should have a much more important role to play in the study of enthymemes. But then curiously, as will also be shown, this plausibilistic type of reasoning was familiar in the ancient world, to the Sophists, as well as to leading philosophers such as Plato and Aristotle. By linking this ancient notion of plausibility to the modern notion used in computer science, this investigation reveals an important basis for the enthymeme that has a type of logical structure in its own right, but also has an informal aspect.An enthymeme, in current usage, is an argument that has one or more premises, or possibly a conclusion, not explicitly stated in the text, but that needs to have these propositions explicitly stated to extract the complete argument from the text. Sometimes enthymemes are described as arguments with "missing premises." That vocabulary is awkward, however, because the nonexplicit statement that needs to be added can be a conclusion, at least in a minority of cases. To make the exposition below smoother, the term nonexplicit assumption will be used to cover either the case of a nonexplicit premise or that of a nonexplicit conclusion. The problem with enthymemes is that if the nonexplicit assumptions in an argument are supposed to be propositions used by the arguer (as opposed to just the propositions needed to make the argument structurally correct, according to some standard), reasonable people can have differences of opinion on what the nonexplicit assumptions are supposed to be. The problem is that filling in the missing parts of enthymemes depends on interpreting the natural language [End Page 93] in which the argument was put forward to try to determine what the speaker meant to say.The solution to the problem comes through the recognition that enthymemes rest not only on formal (structural) criteria, but also on informal criteria. One of the most important of these informal criteria is something often called "common knowledge" (Govier 1992; Freeman 1995). But as shown below in a set of selected case studies of enthymemes, "common knowledge" is not really a kind of knowledge at all. It is really plausibility, or eikos, something well known in the ancient world and often misleadingly translated as "probability." Curiously, Aristotle's original doctrine of the enthymeme was based on this notion of plausibility. This historical fact has often been a source of puzzlement and confusion, and sometimes it has even been taken to indicate a defect or contradiction in Aristotle's treatment of the enthymeme. However, the goal of this investigation is not primarily historical. It is to work out one of the most important required steps toward a solution to the problem of enthymemes. But to do this, it is necessary to come back to the ancient notion of plausible inference. 1. The problem with enthymemes Once you have any formal theory of inference, like syllogistic, for example, you will be confronted by cases of arguments in everyday discourse that meet all the requirements for a structurally correct inference except that some part or parts, usually a premise or a conclusion, is missing. By "missing" is meant that the proposition in question has not been explicitly stated in the text of discourse, even though it may be clear enough that the speaker (writer) was relying on it, or including it, as part of the argument. The classic example is the following inference: All men are mortal; therefore, Socrates is mortal. To evaluate such an argument, surely some account of the "hidden" or "missing" assumption needs to be taken. The problem with enthymemes (Burke 1985; Gough and Tindale 1985; Hitchcock 1985) is that, if given carte... (shrink)

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

It is shown by examples that inductive procedures which are commonly noticed only in the experimental sciences, are heuristically applicable also to purely mathematical questions. Similar processes are pointed out in inventive and everyday reasoning. A simple pattern of plausible inference is formulated and the bearing of these remarks on the current philosophical discussion of probability is hinted at. ‐ G. P.

These two small works are a good supplement to Rescher’s recent trilogy. Whereas the systems-theoretic approach is employed in Methodological Pragmatism in dealing with the problem of the legitimation of claims to factual knowledge or cognitive rationality, Dialectics deals with the argumentation aspect of thesis-introduction rather than the logical aspect of thesis-derivation. Although some key notions such as the idea of burden of proof and presumption have been stated in the former work, what is offered here is a systematic discussion (...) of a disputational model of inquiry. The principal aims are "to exhibit the sociocommunal roots of the foundations of rationality, to provide an instrument for the critique of scepticism implicit in the cognitive solipsism of the Cartesian approach, and to illuminate the communal and controversy-oriented aspects of rational argumentation and inquiry—scientific inquiry in particular." Again, Rescher limits his discussion to "the probative mechanism in the factual domain." This book, if it were not for its price, should be a good supplement to a course in "informal" logic which focuses on argumentation rather than on mere evaluation of arguments. Plausible Reasoning provides the mechanism for evaluation of plausibility claims as distinct from probabilistic ones. The basic differences between these two types of claims are clearly discussed. "Plausibility is essentially a classificatory concept which ranks theses in terms of the standing and solidity of their cognitive basis. Plausibility grades theses by an external or extrinsic standard of the hierarchical nature of their supporting bases. It classifies propositions by the status of the evidential sources or validating principles that vouch for them. Probability weighs alternatives and evaluates theses by this relative contentual weight of the supporting considerations."—A.S.C. (shrink)

When cognitive neuropsychologists make inferences about the functional architecture of the normal mind from selective cognitive impairments they generally assume that the effects of brain damage are local, that is, that the nondamaged components of the architecture continue to function as they did before the damage. This assumption follows from the view that the components of the functional architecture are modular, in the sense of being informationally encapsulated. In this target article it is argued that this “locality” assumption is probably (...) not correct in general. Inferences about the functional architecture can nevertheless be made from neuropsychological data with an alternative set of assumptions, according to which human information processing is graded, distributed, and interactive. These claims are supported by three examples of neuropsychological dissociations and a comparison of the inferences obtained from these impairments with and without the locality assumption. The three dissociations are: selective impairments in knowledge of living things, disengagment of visual attention, and overt face recognition. In all three cases, the neuropsychological phenomena lead to more plausible inferences about the normal functional architecture when the locality assumption is abandoned. Also discussed are the relations between the locality assumption in neuropsychology and broader issues, including Fodor's modularity hypothesis and the choice between top-down and bottom-up research approaches. (shrink)

When cognitive neuropsychologists make inferences about the functional architecture of the normal mind from selective cognitive impairments they generally assume that the effects of brain damage are local, that is, that the nondamaged components of the architecture continue to function as they did before the damage. This assumption follows from the view that the components of the functional architecture are modular, in the sense of being informationally encapsulated. In this target article it is argued that this “locality” assumption is probably (...) not correct in general. Inferences about the functional architecture can nevertheless be made from neuropsychological data with an alternative set of assumptions, according to which human information processing is graded, distributed, and interactive. These claims are supported by three examples of neuropsychological dissociations and a comparison of the inferences obtained from these impairments with and without the locality assumption. The three dissociations are: selective impairments in knowledge of living things, disengagment of visual attention, and overt face recognition. In all three cases, the neuropsychological phenomena lead to more plausible inferences about the normal functional architecture when the locality assumption is abandoned. Also discussed are the relations between the locality assumption in neuropsychology and broader issues, including Fodor's modularity hypothesis and the choice between top-down and bottom-up research approaches. (shrink)

Plausibly, an inference is an act of coming to believe something on the basis of something else you already believe. But what is it to come to believe some- thing on the basis of something else? I propose a disjunctive answer: it is either for some beliefs to rationally cause another—where rational causation is understood as causation that is either actually or potentially productive of knowledge—or for some beliefs to “deviantly” cause another, but for the believer mistakenly to come (...) thereby to believe that the former have rationally caused the latter. The result, I argue, is both a theoretically satisfying account of the act of inferring and a demonstration of the power of a knowledge-first approach to the philosophy of mind. (shrink)

Current practice in logic increasingly accords recognition to abductive, presumptive or plausible arguments, in addition to deductive and inductive arguments. But there is uncertainty about what these terms exactly mean, what the differences between them are (if any), and how they relate. By examining some analyses ofthese terms and some of the history of the subject (including the views of Peirce and Cameades), this paper sets out considerations leading to a set of definitions, discusses the relationship of these three (...) forms of argument to argumentation schemes and sets out a new argumentation scheme for abductive argument. (shrink)

In informal terms, abductive reasoning involves inferring the best or most plausible explanation from a given set of facts or data. It is a common occurrence in everyday life and crops up in such diverse places as medical diagnosis, scientific theory formation, accident investigation, language understanding, and jury deliberation. In recent years, it has become a popular and fruitful topic in artificial intelligence research. This volume breaks new ground in the scientific, philosophical, and technological study of abduction. It presents (...) new ideas about inferential and information-processing foundations for knowledge and certainty. The authors argue that knowledge arises from experience by processes of abductive inference, in contrast to the view that it arises non-inferentially, or that deduction and inductive generalization are enough to account for knowledge. Much AI research is hypothetical, so the importance of this book is that it reports key discoveries about abduction that have been made as a result of designing, building, testing, and analyzing actual working knowledge-based systems for medical diagnosis and other abductive tasks. The book tells the story of six generations of increasingly sophisticated generic abduction machines, RED-1, RED-2, PEIRCE, MDX2, TIPS, QUAWDS, and the discovery of reasoning strategies that make it computationally feasible to form well-justified composite explanatory hypotheses, despite the threat of combinatorial explosion. The final chapter argues that perception is logically abductive and presents a layered-abduction computational model of perceptual information processing. This book will be of great interest to researchers in AI, cognitive science, and philosophy of science. (shrink)

Inference to the Best Explanation (IBE) is widely criticized for being an unreliable form of ampliative inference – partly because the explanatory hypotheses we have considered at a given time may all be false, and partly because there is an asymmetry between the comparative judgment on which an IBE is based and the absolute verdict that IBE is meant to license. In this paper, I present a further reason to doubt the epistemic merits of IBE and argue that (...) it motivates moving to an inferential pattern in which IBE emerges as a degenerate limiting case. Since this inferential pattern is structurally similar to an argumentative strategy known as Inferential Robustness Analysis (IRA), it effectively combines the most attractive features of IBE and IRA into a unified approach to non-deductive inference. (shrink)

What is involved in making an inference? This chapter argues against what Paul Boghossian calls the Taking Condition: "Inferring necessarily involves the thinker taking his premises to support his conclusion and drawing his conclusion because of that fact" (2014: 5). I won’t argue that the Taking Condition is incoherent: that nothing can coherently play the role that takings are supposed to play in inference. Instead, I’ll argue that it cannot plausibly explain all the inferential knowledge that we ordinarily (...) take ourselves to have. Moreover, I’ll argue that we don’t need it to understand the nature of inference. (shrink)

2014 Reprint of 1954 American Edition. Full facsimile of the original edition, not reproduced with Optical Recognition Software. This two volume classic comprises two titles: "Patterns of Plausible Inference" and "Induction and Analogy in Mathematics." This is a guide to the practical art of plausible reasoning, particularly in mathematics, but also in every field of human activity. Using mathematics as the example par excellence, Polya shows how even the most rigorous deductive discipline is heavily dependent on techniques (...) of guessing, inductive reasoning, and reasoning by analogy. In solving a problem, the answer must be guessed at before a proof can be given, and guesses are usually made from a knowledge of facts, experience, and hunches. The truly creative mathematician must be a good guesser first and a good prover afterward; many important theorems have been guessed but no proved until much later. In the same way, solutions to problems can be guessed, and a god guesser is much more likely to find a correct solution. This work might have been called "How to Become a Good Guesser."-From the Dust Jacket. (shrink)

ABSTRACT: A traditional objection to inferentialism states that not all inferences can be meaning-constitutive and therefore inferentialism has to comprise an analytic-synthetic distinction. As a response, Peregrin argues that meaning is a matter of inferential rules and only the subset of all the valid inferences for which there is a widely shared corrective behaviour corresponds to rules and so determines meaning. Unfortunately, Peregrin does not discuss what counts as “widely shared”. In the paper, I argue for an empirical plausibility of (...) Peregrin’s proposal. The aim of the paper is to show that we can find examples of meaning-constitutive linguistic action, which sustain Peregrin’s response. The idea is supported by examples of meaning modulation. If Peregrin is right, then we should be able to find specific meaning modulations in which a new meaning is publicly available and modulated in such a way that it has a potential to be widely shared. I believe that binding modulations – a specific type of meaning modulations – satisfy this condition. (shrink)

Chastain and Sawyer, among others, claim that direct cognitive relations can be initiated in evidence cases. Direct cognitive relations will here include Chastain’s knowledge-of and Sawyer’s trace-based acquaintance, as well as related notions such as having-in-mind and singular thought. Against this controversial claim, it is often objected that such cases are better understood as cases of inference rather than cases of direct thought. When one detects something by its footprint, the objection goes, one merely infers that it exists rather (...) than thinking of it directly. The goal of this paper is to analyze what is meant by the inference objection and consider several possible responses to it. Ultimately, I will not offer a knock-down argument against the inference explanation; in fact, I’ll try to explain why I suspect one isn’t possible. Instead, I’ll appeal to the possibility of misdescription and analogous cases involving non-human animals to show that the inference explanation is less plausible than the account to which it provides an alternative. (shrink)

I focus on a family of inferences that are intuitively incapable of producing knowledge of their conclusions, although they appear to satisfy sufficient conditions for inferential knowledge postulated by plausible epistemological theories. They include Moorean inferences and inductive-bootstrapping inferences. I provide an account of why these inferences are not capable of producing knowledge. I argue that the reason why these inferences fail to produce knowledge of their conclusions is that inferential knowledge requires that the subject is more likely to (...) believe the premises of the inference if the conclusion is true than if it is false. I end by comparing the treatment of these cases that emerges from the approach that I recommend with the position advocated by Sherrilyn Roush in her recent book, Tracking Truth (2005). (shrink)

In pedagogical contexts and in everyday life, we often come to believe something because it would best explain the data. What is it about the explanatory endeavor that makes it essential to everyday learning and to scientific progress? There are at least two plausible answers. On one view, there is something special about having true explanations. This view is highly intuitive: it’s clear why true explanations might improve one’s epistemic position. However, there is another possibility—it could be that the (...) process of seeking, generating, or evaluating explanations itself puts one in a better epistemic position, even when the outcome of the process is not a true explanation. In other words, it could be that accurate explanations are beneficial, or it could be that high-quality explaining is beneficial, where there is something about the activity of looking for an explanation that improves our epistemic standing. The main goal of this paper is to tease apart these two possibilities, both theoretically and empirically, which we align with “Inference to the Best Explanation” and “Explaining for the Best Inference”, respectively. We also provide some initial support for EBI and identify promising directions for future research. (shrink)

ABSTRACT There is a new argument form within theoretical biology. This form takes as input competing explanatory models; it yields as output the conclusion that one of these models is more plausible than the others. The driving force for this argument form is an analysis showing that one model exhibits more parametric robustness than its competitors. This article examines these inferences to the more robust explanation, analysing them as variants of inference to the best explanation. The article defines (...) parametric robustness and distinguishes it from more familiar kinds of robustness. The article also argues that parametric robustness is an explanatory virtue not subsumed by more familiar explanatory virtues, and that the plausibility verdicts in the conclusions of inferences to the more robust explanations are best interpreted as guidance for research activity, rather than claims about likely truth. 1Introducing Inference to the More Robust Explanation 2Inference to the More Robust Explanation in the Study of Apoptosis 2.1Regulating apoptosis 2.2Competing models and evidential indecision 2.3Measuring robustness 2.4Robustness as a guide to plausibility 2.5Varieties of robustness 3Inference to the More Robust Explanation as Inference to the Best Explanation 3.1The structure of inference to the best explanation 3.2Parametric robustness as an explanatory virtue rather than an explanandum 3.3Relation of parametric robustness to other explanatory virtues 4Epistemological Significance of Inference to the More Robust Explanation 4.1Plausibility in practice 4.2Plausibility in principle 5Conclusion. (shrink)

In Lehrer’s case of the superstitious lawyer, a lawyer possesses conclusive evidence for his client’s innocence, and he appreciates that the evidence is conclusive, but the evidence is causally inert with respect to his belief in his client’s innocence. This case has divided epistemologists ever since Lehrer originally proposed it in his argument against causal analyses of knowledge. Some have taken the claim that the lawyer bases his belief on the evidence as a data point for our theories to accommodate, (...) while others have denied that the lawyer has knowledge, or that he bases his belief on the evidence. In this paper, we move the dialectic forward by way of arguing that the superstitious lawyer genuinely infers his client’s innocence from the evidence. To show that the lawyer’s inference is genuine, we argue in defense of a version of a doxastic construal of the ‘taking’ condition on inference. We also provide a pared-down superstitious lawyer-style case, which displays the key features of the original case without including its complicated and distracting features. But interestingly, although we argue that the lawyer’s belief is based on his good evidence, and is also plausibly doxastically justified, we do not argue that the lawyer knows that his client is innocent. (shrink)

Inference to the best explanation has at times appeared almost indistinguishable from a rule that recommends simply that we should infer the hypothesis which is most plausible given available evidence. In this paper I argue that avoiding this collapse requires the identification of peculiarly explanatory virtues and consider Woodward's concept of invariance as an example of such a virtue. An additional benefit of augmenting IBE with Woodward's model of causal explanation is also suggested.

The plausible reasoning class (called the JSM-reasoning in honour of John Stuart Mill) is described. It implements interaction of three forms of non-deductive procedures  induction, analogy and abduction. Empirical induction in the JSM-reasoning is the basis for generation of hypotheses on causal relations (determinants of social behaviour). Inference by analogy means that predictions about previously unknown properties of objects (individual’s behaviour) are inferred from causal relations. Abductive inference is performed to check on the explanatory adequacy of (...) generated hypotheses. To recognize rationality of respondents’ opinion deductive inference is used. Plausible reasoning, semantics of argumentation logic and deductive recognition of opinion rationality represent logical tool for cognitive sociology problems. (shrink)

The common role of research programs in science and religion is now widely accepted. The next step in the methodology debate is to specify more concretely the shared standards for adequate explanations. The article presents a detailed account of the method of inference to the best explanation and gives examples of how the method can structure the philosophical and theological interaction with science. The resulting approach dispenses with deductive and inductive proofs of religious propositions and limits itself to initially (...) plausible hypotheses that are to be assessed according to their explanatory power. Only when a domain of data and a particular explanatory task have been specified can any serious claim be made that religious theories are equal or superior to their naturalistic alternatives. (shrink)

The evidential role of experience in justifying beliefs has been at the center of debate in philosophy in recent years. One view is that experience, or seeming, can confer immediate justification on belief in virtue of its representational phenomenology. Call this view “representational dogmatism.” Another view is that experience confers immediate justification on belief in virtue of its relational phenomenology. Call this view “relational dogmatism.” The goal of this paper is to pit these two versions of dogmatism against each other (...) in terms of their ability to account for ampliative, or non-deductive, inferential justification. I will argue that only the representational view can provide a plausible account of this type of justification. (shrink)

1. The inference that the real is like the phenomenal is the only plausible inference. Since experience itself is the basis for inference, and since we have never experienced what is beyond experience, we have no basis within experience for assuming that what is beyond is unlike experience. The search for what is beyond experience is futile, for it can result only in other experiences. Each additional experience, including those in which we think about what is (...) beyond experience, is still experience. The piling up of empirical evidence merely strengthens the argument, for it provides further evidence of uniformity. To obtain evidence that what is beyond experience is unlike experience, one would have to stop experiencing, which is impossible. (shrink)

How are inferences to design affected when one makes the (plausible) assumption that the universe is spatially infinite? I will show that arguments for the existence of God based on the improbable development of life don’t go through. I will also show that the model of design inferences promulgated by William Dembski is flawed. My model for design inferences has the (desirable) consequence that there are circumstances where a seeming miracle can count as evidence for the existence of God, (...) even if one would expect that type of event to naturalistically occur in a spatially infinite universe. (shrink)

This paper considers how we decide whether to believe what we are told. Inference to the Best Explanation, a popular general account of non-demonstrative reasoning, is applied to this task. The core idea of this application is that we believe what we are told when the truth of what we are told would figure in the best explanation of the fact that we were told it. We believe the fact uttered when it is part of the best explanation of (...) the fact of utterance. Having provided some articulation of this account of testimonial inference, the paper goes on to consider whether the account is informative and whether it is plausible. (shrink)

To speak of “inferences,” “interpretations,” and so forth is just folk psychology. It creates new homunculi, and it is also implausible from a purely phenomenological perspective. Phenomenal volition must be described in the conceptual framework of an empirically plausible theory of mental representation. It is a non sequitur to conclude from dissociability that the functional properties determining phenomenal volition never make a causal contribution.

In recent decades “inference to the best explanation” has become a fashionable mode of reasoning. It is, however, highly problematic and flawed in ways that the article expounds. Instead, so it is argued, a process of inference from the best systematization of the relevant determinable facts is a far more plausible and promising procedure.

This book grew out of previously published papers of mine composed over a period of years; they have been reworked (sometimes beyond recognition) so as to form a reasonably coherent whole. Part One treats of informative inference. I argue (Chapter 2) that the traditional principle of induction in its clearest formulation (that laws are confirmed by their positive cases) is clearly false. Other formulations in terms of the 'uniformity of nature' or the 'resemblance of the future to the past' (...) seem to me hopelessly unclear. From a Bayesian point of view, 'learning from experience' goes by conditionalization (Bayes' rule). The traditional stum bling block for Bayesians has been to fmd objective probability inputs to conditionalize upon. Subjective Bayesians allow any probability inputs that do not violate the usual axioms of probability. Many subjectivists grant that this liberality seems prodigal but own themselves unable to think of additional constraints that might plausibly be imposed. To be sure, if we could agree on the correct probabilistic representation of 'ignorance' (or absence of pertinent data), then all probabilities obtained by applying Bayes' rule to an 'informationless' prior would be objective. But familiar contra dictions, like the Bertrand paradox, are thought to vitiate all attempts to objectify 'ignorance'. BuUding on the earlier work of Sir Harold Jeffreys, E. T. Jaynes, and the more recent work ofG. E. P. Box and G. E. Tiao, I have elected to bite this bullet. In Chapter 3, I develop and defend an objectivist Bayesian approach. (shrink)

The idea that knowledge can be extended by inference from what is known seems highly plausible. Yet, as shown by familiar preface paradox and lottery-type cases, the possibility of aggregating uncertainty casts doubt on its tenability. We show that these considerations go much further than previously recognized and significantly restrict the kinds of closure ordinary theories of knowledge can endorse. Meeting the challenge of uncertainty aggregation requires either the restriction of knowledge-extending inferences to single premises, or eliminating epistemic (...) uncertainty in known premises. The first strategy, while effective, retains little of the original idea—conclusions even of modus ponens inferences from known premises are not always known. We then look at the second strategy, inspecting the most elaborate and promising attempt to secure the epistemic role of basic inferences, namely Timothy Williamson’s safety theory of knowledge. We argue that while it indeed has the merit of allowing basic inferences such as modus ponens to extend knowledge, Williamson’s theory faces formidable difficulties. These difficulties, moreover, arise from the very feature responsible for its virtue- the infallibilism of knowledge. (shrink)

According to the received view in epistemology, inferential knowledge from non-knowledge is impossible – that is, in order for a subject to know the conclusion of their inference, they must know the essential premises from which that conclusion is drawn. In this book, Federico Luzzi critically examines this view, arguing that it is less plausible than intuition suggests and that it can be abandoned without substantial cost. In a discussion that ranges across inference, testimony and memory he (...) analyses the full range of challenges to the view, connecting them to epistemological cases that support those challenges. He then proposes a defeater-based framework which allows the phenomenon of knowledge from non-knowledge across these three epistemic areas to be better understood. His book will be of interest to a wide range of readers in epistemology. (shrink)

A group of philosophers led by the late John Pollock has applied a method of reasoning about probability, known as direct inference and governed by a constraint known as Reichenbach's principle, to argue in support of ‘thirdism’ concerning the Sleeping Beauty Problem. A subsequent debate has ensued about whether their argument constitutes a legitimate application of direct inference. Here I defend the argument against two extant objections charging illegitimacy. One objection can be overcome via a natural and (...) class='Hi'>plausible definition, given here, of the binary relation ‘logically stronger than’ between two properties that can obtain even when the respective properties differ from one another in ‘arity’; given this definition, the Pollock group's argument conforms to Reichenbach's principle. Another objection prompts a certain refinement of Reichenbach's principle that is independently well-motivated. My defense of the Pollock group's argument has epistemological import beyond the Sleeping Beauty problem, because it both widens and sharpens the applicability of direct inference as a method for inferring single-case epistemic probabilities on the basis of general information of a probabilistic or statistical nature. (shrink)

This paper addresses a common challenge with computational cognitive models: identifying parameter values that are both theoretically plausible and generate predictions that match well with empirical data. While computational models can offer deep explanations of cognition, they are computationally complex and often out of reach of traditional parameter fitting methods. Weak methodology may lead to premature rejection of valid models or to acceptance of models that might otherwise be falsified. Mathematically robust fitting methods are, therefore, essential to the progress (...) of computational modeling in cognitive science. In this article, we investigate the capability and role of modern fitting methods—including Bayesian optimization and approximate Bayesian computation—and contrast them to some more commonly used methods: grid search and Nelder–Mead optimization. Our investigation consists of a reanalysis of the fitting of two previous computational models: an Adaptive Control of Thought—Rational model of skill acquisition and a computational rationality model of visual search. The results contrast the efficiency and informativeness of the methods. A key advantage of the Bayesian methods is the ability to estimate the uncertainty of fitted parameter values. We conclude that approximate Bayesian computation is (a) efficient, (b) informative, and (c) offers a path to reproducible results. (shrink)