In this paper I draw from Martin Heidegger’s phenomenology of the 1920s to outline some basic features of his theory of intentionality that I believe have not been fully appreciated or utilized, and that allow for both novel and fruitful interventions in questions about meaning, the relationship between mind and the world, and epistemic justification, principally as they appear in John McDowell’s synoptic project in Mind and World. I argue that while elements of McDowell’s picture are ultimately unsatisfying and problematic, (...) much of his conceptual framework can and should be put into dialogue with Heidegger’s, and that in so doing we make available powerful resources for amending the McDowellian account. Moreover, these emendations have attractive implications for his distinctive desiderata. In particular, they provide original conceptions of normativity’s place in nature, of the boundaries of the space of reasons, and of the relationship between the answerability of thought both to the world and to human beings as a rational community. (shrink)

The objective of this paper is to construct an implementable theory of rational decision-making for cognitive agents subject to realistic resource constraints. It is argued that decision-making should select actions indirectly by selecting plans that prescribe them. It is also argued that although expected values provide the tool for evaluating plans, plans cannot be compared straightforwardly in terms of their expected values, and the objective of a realistic agent cannot be to find optimal plans. The theory of Locally Global (...) planning is proposed as a realistic alternative to standard "maximizing" theories of rational decision-making. (shrink)

There have been two major lines of research aimed at capturing resource-bounded players in game theory. The first, initiated by Rubinstein (), charges an agent for doing costly computation; the second, initiated by Neyman (), does not charge for computation, but limits the computation that agents can do, typically by modeling agents as finite automata. We review recent work on applying both approaches in the context of decision theory. For the first approach, we take the objects of choice (...) in a decision problem to be Turing machines, and charge players for the “complexity” of the Turing machine chosen (e.g., its running time). This approach can be used to explain well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on) and belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions) as the outcomes of quite rational decisions. For the second approach, we model people as finite automata, and provide a simple algorithm that, on a problem that captures a number of settings of interest, provably performs optimally as the number of states in the automaton increases. (shrink)

Agents need to be able to change their beliefs; in particular, they should be able to contract or remove a certain belief in order to restore consistency to their set of beliefs, and revise their beliefs by incorporating a new belief which may be inconsistent with their previous beliefs. An influential theory of belief change proposed by Alchourron, G¨ardenfors and Makinson (AGM) [1] describes postulates which a rational belief revision and contraction operations should satisfy. The AGM postulates have been perceived (...) as characterising idealised rational reasoners, and the corresponding belief change operations are considered unsuitable for implementable agents due to their high computational cost [3]. The main result of this paper is showing that an efficient (linear time) belief contraction operation nevertheless satisfies all but one of the AGM postulates for contraction. This contraction operation is defined for a realistic rule-based agent which can be seen as a reasoner in a very weak logic; although the agent’s beliefs are deductively closed with respect to this logic, checking consistency and tracing dependencies between beliefs is not computationally expensive. Finally, we give a non-standard definition of belief revision in terms of contraction for our agent. (shrink)

In the Newcomb problem, the standard arguments for taking either one box or both boxes adduce what seem to be relevant considerations, but they are not complete arguments, and attempts to complete the arguments rely upon incorrect principles of rational decision making. It is argued that by considering how the predictor is making his prediction, we can generate a more complete argument, and this in turn supports a form of causal decision theory.

While game theory has been transformative for decision making, the assumptions made can be overly restrictive in certain instances. In this work, we investigate some of the underlying assumptions of rationality, such as mutual consistency and best response, and consider ways to relax these assumptions using concepts from level-k reasoning and quantal response equilibrium (QRE) respectively. Specifically, we propose an information-theoretic two-parameter model called the quantal hierarchy model, which can relax both mutual consistency and best response while still approximating (...) level-k, QRE, or typical Nash equilibrium behavior in the limiting cases. The model is based on a recursive form of the variational free energy principle, representing higher-order reasoning as (pseudo) sequential decision-making in extensive-form game tree. This representation enables us to treat simultaneous games in a similar manner to sequential games, where reasoning resources deplete throughout the game-tree. Bounds in player processing abilities are captured as information costs, where future branches of reasoning are discounted, implying a hierarchy of players where lower-level players have fewer processing resources. We demonstrate the effectiveness of the quantal hierarchy model in several canonical economic games, both simultaneous and sequential, using out-of-sample modelling. (shrink)

An increasingly prevalent approach to studying human cognition is to construe the mind as optimally allocating limited cognitive resources among cognitive processes. Under this bounded rationality approach (Icard in Philos Sci 85(1):79–101, 2018; Simon in Utility and probability, Palgrave Macmillan, 1980), it is common to assume that resource-bounded cognitive agents approximate normative solutions to statistical inference problems, and that much of the bias and variability in human performance can be explained in terms of the approximation strategies (...) we employ. In this paper, we argue that this approach restricts itself to an unnecessarily narrow scope of cognitive models, which limits its ability to explain how humans flexibly adapt their representations to novel environments. We argue that more attention should be paid to how we form our cognitive representations in the first place, and advocate for pluralistic framework which jointly optimizes over both representations and algorithms for manipulating them. We identify several fundamental trade-offs that manifest in this joint optimization, and draw on recent work to motivate a unified formal framework for this analysis. We illustrate a simplified version of this analysis with a case study in social cognition, and outline several new directions for research that this approach suggests. (shrink)

In this paper we design a new logical system to explicitly model the different deductive reasoning steps of a boundedly rational agent. We present an adequate system in line with experimental findings about an agent’s reasoning limitations and the cognitive effort that is involved. Inspired by Dynamic Epistemic Logic, we work with dynamic operators denoting explicit applications of inference rules in our logical language. Our models are supplemented by (a) impossible worlds (not closed under logical consequence), suitably structured according to (...) the effect of inference rules, and (b) quantitative components capturing the agent’s cognitive capacity and the cognitive costs of rules with respect to certain resources (e.g. memory, time). These ingredients allow us to avoid problematic logical closure principles, while at the same time deductive reasoning is reflected in our dynamic truth clauses. We further show that our models can be reduced to awareness-like plausibility structures that validate the same formulas and we give a sound and complete axiomatization with respect to them. This approach to the agent’s internal deductive reasoning is finally combined with actions of external information. (shrink)

“Rationality” in Simon's “bounded rationality” is the principle that humans make decisions on the basis of step-by-step reasoning using systematic rules of logic to maximize utility. “Bounded rationality” is the observation that the ability of a human brain to handle algorithmic complexity and large quantities of data is limited. Bounded rationality, in other words, treats a decision maker as a machine carrying out computations with limited resources. Under the principle of embodied cognition, a (...) cognitive mind is an interactive machine. Turing-Church computations are not interactive, and interactive machines can accomplish things that no Turing-Church computation can accomplish. Hence, if “rationality” is computation, and “bounded rationality” is computation with limited complexity, then “embodied bounded rationality” is both more limited than computation and more powerful. By embracing interaction, embodied bounded rationality can accomplish things that Turing-Church computation alone cannot. Deep neural networks, which have led to a revolution in artificial intelligence, are both interactive and not fundamentally algorithmic. Hence, their ability to mimic some cognitive capabilities far better than prior algorithmic techniques based on symbol manipulation provides empirical evidence for the principle of embodied bounded rationality. (shrink)

Theories of rational decision making often abstract away from computational and other resource limitations faced by real agents. An alternative approach known as resource rationality puts such matters front and center, grounding choice and decision in the rational use of finite resources. Anticipated by earlier work in economics and in computer science, this approach has recently seen rapid development and application in the cognitive sciences. Here, the theory of rationality plays a dual role, both as a (...) framework for normative assessment and as a source of scientific hypotheses about how mental processes in fact work. The latter project, often called rational analysis, depends for its success on a fine-grained characterization of the computational problem facing a decision maker, which may in turn depend on realistic assumptions about what the relevant agent is like. As a consequence, resource rationality involves a delicate, but often fruitful interplay between the normative and the descriptive. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 528-549, July 2022.

Previous theory and research on bounded rationality has emphasized how limited cognitive resources constrain people from making utility maximizing choices. This paper expands the concept of bounded rationality to consider how people’s rationality may be constrained by their internalization of a qualitatively distinct standard for sound judgment, which is commonly labeled reasonableness. In contrast to rationality, the standard of reasonableness provides guidance for making choices in situations that involve balancing incommensurable values and interests or (...) reconciling conflicting points-of-view. We review recent evidence showing that laypeople readily recognize the distinctions between rationality and reasonableness and thus are able to utilize these as distinct standards to inform their everyday decision-making. The fact that people appear to have internalized rationality and reasonableness as distinct standards of sound judgment supports the notion that people’s pursuit of rationality may be bounded by their determination to also be reasonable. (shrink)

All forms of cognition, whether natural or artificial, are subject to constraints of their computing architecture. This assumption forms the tenet of virtually all general theories of cognition, including those deriving from bounded optimality and bounded rationality. In this letter, we highlight an unresolved puzzle related to this premise: what are these constraints, and why are cognitive architectures subject to cognitive constraints in the first place? First, we lay out some pieces along the puzzle edge, such as (...) computational tradeoffs inherent to neural architectures that give rise to rational bounds of cognition. We then outline critical next steps for characterizing cognitive bounds, proposing that some of these bounds can be subject to modification by cognition and, as such, are part of what is being optimized when cognitive agents decide how to allocate cognitive resources. We conclude that these emerging views may contribute to a more holistic perspective on the nature of cognitive bounds, as well as their alteration subject to cognition. (shrink)

How much would you pay to play a lottery with an “infinite expected payoff?” In the case of the century old, St. Petersburg Paradox, the answer is that the vast majority of people would only pay a small amount. The authors seek to understand this paradox by providing an explanation consistent with a broad, process‐level model of human decision‐making under risk.

An account of the virtues of limitation management: intellectual virtues of adapting to the fact that we cannot solve many of the problems that we can describe. I argue that the best response to many problems depends not on the most rationally promising solution, but on the most likely route to success. I argue against techniques that assume that one will fulfil ones intentions, and distinguish between failures of rationality and failures of intelligence. I describe the trap of supposing (...) that one will be capable of following the plan that would best exploit ones limited resources. (shrink)

Normativism, the approach that judges human rationality by comparison against normative standards, has recently come under intensive criticism as unsuitable for psychological enquiry, and it has been suggested that it should be replaced with a descriptivist paradigm. My goal in this paper is to outline and defend a meta-theoretical framework of such a paradigm, grounded rationality, based on the related principles of descriptivism and (moderate) epistemic relativism. Bounded rationality takes into account universal biological and cognitive limitations (...) on human rationality. Grounded rationality accepts universal constraints but adds cognitive variability: Within-individual variability (dual processing), and individual as well as cultural differences. I discuss the implications of grounded rationality to dual processing, proposing that investing limited cognitive resources in analytic processing might be less instrumentally rational for individuals with low cognitive ability. (shrink)

When information grows abundant, attention becomes a scarce resource. As a result, agents must plan wisely how to allocate their attention in order to achieve epistemic efficiency. Here, we present a framework for multi-agent epistemic planning with attention, based on Dynamic Epistemic Logic. We identify the framework as a fragment of standard DEL, and consider its plan existence problem. While it is undecidable in the general case, we show that when attention is required for learning, all instances of the (...) problem are decidable. (shrink)

The current notions of bounded rationality in economics share distinctive features with Simon’s original notion of bounded rationality, which still influences the theoretical and experimental research in the fields of choice, judgment, decision making, problem solving, and social cognition. All these notions of bounded rationality are in fact equally rooted in the information-processing approach to human cognition, expressing the view that reasoning is disembodied and that it can be reduced to the processing of abstract (...) symbolic representations of the environment. This is in contrast with the last three-decade advancements in cognitive psychology, where a new view on human cognition has emerged under the general label of ‘embodied cognition’, demonstrating that cognition and reasoning are grounded in the morphological traits of the human body and the sensory-motor system. In this paper we argue that embodied cognition might reform the current notions of bounded rationality and we propose a number of arguments devoted to outline a novel program of research under the label of ‘embodied rationality’: reasoning is situated as it arises from the ongoing interaction between the subject and the environment; reasoning, not being exclusively a mental phenomenon, constitutively relies on the physical resources provided by the environment; the sensory-motor system provides the building blocks for abstract reasoning, automatic thinking is rooted in the evolutionary coupling between the morphological traits of the human body and the environment. (shrink)

We introduce a three-tiered framework for modelling and reasoning about agents who (i) can use possibly complete reasoning systems without any restrictions but who nevertheless are (ii) bounded in the sense that they never reach infinitely many results and, finally, who (iii) perform their reasoning in time. This last aspect does not concern so much the time it takes for agents to actually carry out their reasoning, as the time which can bring about external changes in the agents’ states (...) such as arriving of new information or discarding previously available information due to bounds of the agent’s resources. These three aspects are treated with the maximal possible degree of independence from each other. The treatment of layer (iii) can be combined with arbitrary logic at level (ii) which, in turn, can be combined with arbitrary agent logic at level (i). At the level (iii), we discuss briefly the duality (or rather, complementarity) of system descriptions based on actions and transitions, on the one hand, and states and their changes, on the other. We settle for the latter and present a simple language, for describing state changes, which is parameterized by an arbitrary language for describing properties of the states. The language can be viewed as a simple fragment of step logic, admitting however various extensions by appropriate choices of the underlying logic. Alternatively, it can be seen as a very specific fragment of temporal logic (with a variant of ‘until’ or ‘chop’ operator), and is interpreted over dense linear time. The reasoning system presented here is sound, as well as strongly complete and decidable (provided that so is the parameter logic for reasoning about single states). We give the main idea of the completeness proof and suggest a wide range of possible applications, which is a simple consequence of the parametric character of both the language and the reasoning system. We address then in more detail the case of non-omniscient rational agents, (ii). Their models are syntactic structures (sets of available formulae) similar in spirit, though not in the technical formulation, to the models used in other syntax oriented approaches and in active logic. We give a new sound, complete and decidable system for reasoning about such agents. Finally, we illustrate its extensions with the internal reasoning of agents, (i), by equipping them with some example logics. (shrink)

This chapter contains sections titled: Introduction Rational Agents and the Conceptual Background Beyond Homo economicus Informational Resources A Poisoned Chalice? What Is to Be Done? References.

The objective of this book is to produce a theory of rational decision making for realistically resource-bounded agents. My interest is not in “What should I do if I were an ideal agent?”, but rather, “What should I do given that I am who I am, with all my actual cognitive limitations?” The book has three parts. Part One addresses the question of where the values come from that agents use in rational decision making. The most comon view (...) among philosophers is that they are based on preferences, but I argue that this is computationally impossible. I propose an alternative theory somewhat reminiscent of Bentham, and explore how human beings actually arrive at values and how they use them in decision making. Part Two investigates the knowledge of probability that is required for decision-theoretic reasoning. I argue that subjective probability makes no sense as applied to realistic agents. I sketch a theory of objective probability to put in its place. Then I use that to define a variety of causal probability and argue that this is the kind of probability presupposed by rational decision making. So what is to be defended is a variety of causal decision theory. Part Three explores how these values and probabilities are to be used in decision making. In chapter eight, it is argued first that actions cannot be evaluated in terms of their expected values as ordinarily defined, because that does not take account of the fact that a cognizer may be unable to perform an action, and may even be unable to try to perform it. An alternative notion of “expected utility” is defined to be used in place of expected values. In chapter nine it is argued that individual actions cannot be the proper objects of decision-theoretic evaluation. We must instead choose plans, and select actions indirectly on the grounds that they are prescribed by the plans we adopt. However, our objective cannot be to find plans with maximal expected utilities. Plans cannot be meaningfully compared in that way. (shrink)

Information is a force multiplier. Knowledge of the enemy's capability and intentions may be of far more value to a military force than additional troops or firepower. Situation assessment is the ongoing process of inferring relevant information about the forces of concern in a military situation. Relevant information can include force types, firepower, location, and past, present and future course of action. Situation assessment involves the incorporation of uncertain evidence from diverse sources. These include photographs, radar scans, and other forms (...) of image intelligence, or IMINT; electronics intelligence, or ELINT, derived from characteristics (e.g., wavelength) of emissions generated by enemy equipment; communications intelligence, or COMINT, derived from the characteristics of messages sent by the enemy; and reports from human informants (HUMINT). These sources must be combined to form a model of the situation. The sheer volume of data, the ubiquity of uncertainty, the number and complexity of hypotheses to consider, the high-stakes environment, the compressed time frame, and deception and damage from hostile forces, combine to present a staggeringly complex problem. Even if one could formulate a decision problem in reasonable time, explicit determination of an optimal decision policy exceeds any reasonable computational resources. While it is tempting to drop any attempt at rational analysis and rely purely on simple heuristics, we argue that this can lead to catastrophic outcomes. We present an architecture for a ``complex decision machine'' that performs rational deliberation to make decisions in real time. We argue that resource limits require such an architecture to be grounded in simple heuristic reactive processes. We thus argue that both simple heuristics and complex decision machines are required for effective decision making in real time for complex problems. We describe an implementation of our architecture applied to the problem of military situation assessment. (shrink)

It is known that, on average, people adapt their choice of memory strategy to the subjective utility of interaction. What is not known is whether an individual's choices are boundedly optimal. Two experiments are reported that test the hypothesis that an individual's decisions about the distribution of remembering between internal and external resources are boundedly optimal where optimality is defined relative to experience, cognitive constraints, and reward. The theory makes predictions that are tested against data, not fitted to it. The (...) experiments use a no-choice/choice utility learning paradigm where the no-choice phase is used to elicit a profile of each participant's performance across the strategy space and the choice phase is used to test predicted choices within this space. They show that the majority of individuals select strategies that are boundedly optimal. Further, individual differences in what people choose to do are successfully predicted by the analysis. Two issues are discussed: the performance of the minority of participants who did not find boundedly optimal adaptations, and the possibility that individuals anticipate what, with practice, will become a bounded optimal strategy, rather than what is boundedly optimal during training. (shrink)

The paper begins by providing a game-theoretic reconstruction of Gilbert’s (1989) philosophical critique of Lewis (1969) on the role of salience in selecting conventions. Gilbert’s insight is reformulated thus: Nash equilibrium is insufficiently powerful as a solution concept to rationalize conventions for unboundedly rational agents if conventions are solutions to the kinds of games Lewis supposes. Both refinements to NE and appeals to bounded rationality can plug this gap, but lack generality. As Binmore (this issue) argues, evolutive game (...) theory readily explains the origin of conventional behavior, but that is not Lewis’s project. Gilbert’s critique is generalized by reference to Bacharach’s (2006) work on team reasoning in games. The paper then argues that although Lewis’s account of the rationalization of conventions is shown by the reconstruction of Gilbert’s critique to be incomplete, Gilbert is wrong to conclude that classical (‘eductive’) game theory lacks the resources to explain conformity to conventions among people. A game-theoretic account of the dynamics of socialization, based on Ross’s (2005, 2006) idea of ‘game determination’, rationalizes choices of conventional strategies in overlapping generations contexts, provided agents are products of evolutionary selection and know that other players are also such products. (shrink)

Bounded rationality gets a bad rap in epistemology. It is argued that theories of bounded rationality are overly context‐sensitive; conventionalist; or dependent on ordinary language (Carr, 2022; Pasnau, 2013). In this paper, I have three aims. The first is to set out and motivate an approach to bounded rationality in epistemology inspired by traditional theories of bounded rationality in cognitive science. My second aim is to show how this approach can answer recent (...) challenges raised for theories of bounded rationality. My third aim is to clarify the role of rational ideals in bounded rationality. (shrink)

Resource Bounded Agents Resource bounded agents are persons who have information processing limitations. All persons and other cognitive agents who have bodies are such that their sensory transducers have limited resolution and discriminatory ability; their information processing speed and power is bounded by some threshold; and their memory and … Continue reading Resource Bounded Agents →.

Bounded rationality is a fundamental feature of cognition. We make choices between alternatives in light of our goals, relying on incomplete information and limited resources. As a consequence, PROBLEM SOLVING cannot be exhaustive: we cannot explore all the possibilities which confront us, and search must be constrained in ways that facilitate search efficiency even at the expense of search effectiveness. If we think of problem solving as a search through the space of possibilities as it was conceptualized by (...) Allen Newell and Herbert A. Simon, limitations on search entail that for all but the simplest problems we can investigate at most a small proportion of the possibilities. Moreover, our evaluation of these possibilities will be uncertain and incomplete. We must rely on heuristic methods for pruning the tree of possibilities and for evaluating the possibilities we consider. In medical diagnosis, for example, the problem is one of inferring what disease is present from a given set of symptoms, when no one symptom is invariably present with a given disease, and the same symptom can have various causes. Moreover, patients can be suffering from more than one disease, a fact which compounds the difficulties of accurate diagnosis. Given that symptoms cannot generally be assumed to be independent and are noisy indicators of the underlying disease, the size of the search space turns out to be large, and inference concerning underlying diseases is inevitably uncertain. One solution is to limit the range of the search, eliminating some candidates as implausible; that is, we may adopt assumptions limiting the range of diseases considered to a narrow range of those antecedently likely. Good diagnosticians evidently narrow the range to no more than a handful of candidates. Search must be limited in order to be efficient. (shrink)

p. cm. — (Zeuthen lecture book series) Includes bibliographical references (p. ) and index. ISBN 0-262-18187-8 (hardcover : alk. paper). — ISBN 0-262-68100-5 (pbk. : alk. paper) 1. Decision-making. 2. Economic man. 3. Game theory. 4. Rational expectations (Economic theory) I. Title. II. Series.

Is there such a thing as bounded rationality? I first try to make sense of the question, and then to suggest which of the disambiguated versions might have answers. We need an account of bounded rationality that takes account of detailed contingent facts about the ways in which human beings fail to perform as we might ideally want to. But we should not think in terms of rules or norms which define good responses to an individual's (...) limitations, but rather in terms of desiderata, situations that limited agents can hope to achieve, and corresponding virtues of achieving them. We should not take formal theories defining optimal behavior in watered-down bounded form, even though they can impose enormous computational or cognitive demands. (shrink)

Decision theory seems to offer a very attractive normative framework for individual and social choice under uncertainty. The decisionmaker should think of her choice situation, at any given moment, in terms of a set of possible outcomes, that is, specifications of the possible consequences of choice, described in light of the decisionmaker's goals; a set of possible actions; and a "state set" consisting of possible prior "states of the world." It is this framework for choice which provides the foundation for (...) expected utility theory, as demonstrated in the work of Leonard Savage. Problems arise, however, when the decisionmaker is boundedly rational: when the mental process of thinking about outcomes, actions, and states is itself expensive and time consuming. In the case of the unboundedly rational decisionmaker, decision theory enjoins her to employ maximally specific outcomes; to consider all possible actions; and to use a set of mutually exclusive and collective exhaustive states, each of which is sufficiently finely specified so that each action, together with each state, yields one and only one maximally specific outcome. In the case of the boundedly rational decisionmaker, this procedure is either infeasible or, if feasible, irrational. This paper presents the problem of bounded rationality. It surveys possible solutions, none of which are found to be attractive. And it concludes by discussing the difficulties that the problem of bounded rationality poses for the welfarist program for legal scholarship presented by Louis Kaplow and Steven Shavell in their book, Fairness versus Welfare. (shrink)

Research on bounded rationality has two cultures, which I call ‘idealistic’ and ‘pragmatic’. Technically, the cultures differ on whether they build models based on normative axioms or empirical facts, assume that people's goal is to optimize or to satisfice, do not or do model psychological processes, let parameters vary freely or fix them, aim at explanation or prediction and test models from one or both cultures. Each culture tells a story about people's rationality. The story of the (...) idealistic culture is frustrating, with people in principle being able to know what they should do, but in practice systematically failing to do it. This story makes one hide in books for intellectual solace or surrender to the designs of someone smarter. The story of the pragmatic culture is empowering: If people are educated to use the right tool in the right situation, they do well. (shrink)

The inevitable need for rationing of healthcare has apparently presented the medical profession with the dilemma of choosing the lesser of two evils. Physicians appear to be obliged to adopt either an implausible version of traditional professional ethics or an equally problematic ethics of bedside rationing. The former requires unrestricted advocacy of patients but prompts distrust, moral hazard and unfairness. The latter commits physicians to rationing at the bedside; but it is bound to introduce unfair inequalities among patients and lack (...) of political accountability towards citizens. In this paper I shall argue that this dilemma is false, since a third intermediate alternative exists. This alternative, which I term 'administrative gatekeeping', makes it possible for physicians to be involved in rationing while at the same time being genuine advocates of their patients. According to this ideal, physicians are required to follow fair rules of rationing adopted at higher organizational levels within healthcare systems. At the same time, however, they are prohibited from including considerations of cost in their clinical decisions. (shrink)

The notion of bounded rationality has recently gained considerable popularity in the behavioural and social sciences. This article surveys the different usages of the term, in particular the way ‘anomalosus’ behavioural phenomena are elicited, how these phenomena are incorporated in model building, and what sort of new theories of behaviour have been developed to account for bounded rationality in choice and in deliberation. It also discusses the normative relevance of bounded rationality, in particular as (...) a justifier of non‐standard reasoning and deliberation heuristics. For each of these usages, the overview discusses the central methodological problems. (shrink)

The bounded rationality theory has been perceived by social scientists as a more flexible version of the rational choice theory, also called expected utility theory. The former has the avantage of taking into consideration the fact that information is generally costly. It corrects the RCT on an important point. For the social sciences, the RCT is very useful, but far from representing a general theory which could explain the various kinds of behaviour the social sciences are confronted to, (...) even in its more flexible BRT version. The article makes the postulates underlying the RCT and the BRT explicit and shows that, when some of the postulates are eliminated, a genuinely general model is obtained: the model which is actually used in convincing sociological analyses.La Théorie de la rationalité limitée a été perçue par les sciences sociales comme une version assouplie de la théorie du choix rationnel, aussi appelée Modèle de l’utilité espérée. La TRL a l’avantage de tenir compte notamment du fait que l’information est généralement coûteuse, corrigeant ainsi l’irréalisme de la TCR sur un point essentiel. Du point de vue des sciences sociales, la TCR est fort utile, mais elle est loin de proposer une théorie générale de la rationalité permettant de rendre compte des divers types de comportement dont les sciences sociales ont à connaître, même dans sa version assouplie de la TRL. On identifie ici les postulats de la TCR et de la TRL et l’on montre qu’en abandonnant certains de ces postulats, l’on définit un modèle, le MRG qui, lui, peut prétendre à la généralité. C’est celui qui est utilisé naturellement par les analyses sociologiques qui ses sont imposées. (shrink)

Álvarez J.F. (2016) Conflicts, Bounded Rationality and Collective Wisdom in a Networked Society. In: Scarafile G., Gruenpeter Gold L. (eds) Paradoxes of Conflicts. Logic, Argumentation & Reasoning (Interdisciplinary Perspectives from the Humanities and Social Sciences), vol 12. Springer, Cham -/- The adoption of an individualistic perspective on reasoning, choice and decision is a spring of paradoxes of conflicts. Usually the agents immerse in conflicts are drawn or modelled as rational individuals with targets well defined and full capabilities to (...) access to information, without both temporal limitations and perfect reasoning abilities to obtain their preferences are taken account. -/- However, other models of agent, in the bounded rationality perspective, could help to understand better the interrelationships. I adopt embedded argumentative reasoning processes as satisfying criteria to analyze the expert function in a new socio technical environment that has changed deeply the mechanism and tools to access and to aggregate information. The open access to information and institutional arrangements addressed towards team knowledge could offer other kind of tools to affront the conflict, even its possible benefits. -/- The “crowd expertise” is emerging as an actual possibility and it must be incorporated to affront with conflicts. The very possibility of obtaining knowledge generated by “many minds”, collective wisdoms, brings up a real challenge to the conservative or elitist conception of the masses, because masses now emerge as a smart collective user, with new mechanisms to select and produce quality knowledge. These new collective actions differ deeply from the traditional modes of social organization. A new mass society is emerging now as a hybrid one that breaks some conceptual traditional models, such as Ortega y Gasset’s ones, and induces a structured way of flourishing both new practices and new knowledge with transforming capabilities. (shrink)

This paper presents an axiomatic framework for the priority heuristic, a model of bounded rationality in Selten’s (in: Gigerenzer and Selten (eds.) Bounded rationality: the adaptive toolbox, 2001) spirit of using empirical evidence on heuristics. The priority heuristic predicts actual human choices between risky gambles well. It implies violations of expected utility theory such as common consequence effects, common ratio effects, the fourfold pattern of risk taking and the reflection effect. We present an axiomatization of a (...) parameterized version of the heuristic which generalizes the heuristic in order to account for individual differences and inconsistencies. The axiomatization uses semiorders (Luce, Econometrica 24:178–191, 1956), which have an intransitive indifference part and a transitive strict preference component. The axiomatization suggests new testable predictions of the priority heuristic and makes it easier for theorists to study the relation between heuristics and other axiomatic theories such as cumulative prospect theory. (shrink)

The AGM paradigm for belief revision provides a very elegant and powerful framework for reasoning about idealized agents. The paradigm assumes that the modeled agent is a perfect reasoner with infinite memory. In this paper we propose a framework to reason about non-ideal agents that generalizes the AGM paradigm. We first introduce a structure to represent an agent's belief states that distinguishes different status of beliefs according to whether or not they are explicitly represented, whether they are currently active and (...) whether they are fully accepted or provisional. Then we define a set of basic operations that change the status of beliefs and show how these operations can be used to model agents with different capacities. We also show how different operations of belief change described in the literature can be seen as special cases of our theory. (shrink)

We consider several puzzles of bounded rationality. These include the Allais- and Ellsberg paradox, the disjunction effect, and related puzzles. We argue that the present account of quantum cognition—taking quantum probabilities rather than classical probabilities—can give a more systematic description of these puzzles than the alternate treatments in the traditional frameworks of bounded rationality. Unfortunately, the quantum probabilistic treatment does not always provide a deeper understanding and a true explanation of these puzzles. One reason is that (...) quantum approaches introduce additional parameters which possibly can be fitted to empirical data but which do not necessarily explain them. Hence, the phenomenological research has to be augmented by responding to deeper foundational issues. In this article, we make the general distinction between foundational and phenomenological research programs, explaining the foundational issue of quantum cognition from the perspective of operational realism. This framework is motivated by assuming partial Boolean algebras. They are combined into a uniform system via a mechanism preventing the simultaneous realization of perspectives. Gleason’s theorem then automatically leads to a distinction between probabilities that are defined by pure states and probabilities arising from the statistical mixture of pure states. This formal distinction relates to the conceptual distinction between risk and ignorance. Another outcome identifies quantum aspects in dynamic macro-systems using the framework of symbolic dynamics. Finally, we discuss several ideas that are useful for justifying complementarity in cognitive systems. (shrink)

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we (...) illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality. (shrink)

This paper presents statistical default logic, an expansion of classical (i.e., Reiter) default logic that allows us to model common inference patterns found in standard inferential statistics, including hypothesis testing and the estimation of a populations mean, variance and proportions. The logic replaces classical defaults with ordered pairs consisting of a Reiter default in the first coordinate and a real number within the unit interval in the second coordinate. This real number represents an upper-bound limit on the probability of accepting (...) the consequent of an applied default and that consequent being false. A method for constructing extensions is then defined that preserves this upper bound on the probability of error under a (skeptical) non-monotonic consequence relation. (shrink)

Decision theory aims at a general account of rationality covering humans but to begin makes idealizations about decision problems and agents' resources and circumstances. It treats inerrant agents with unlimited cognitive power facing tractable decision problems. This book systematically rolls back idealizations and without loss of precision treats errant agents with limited cognitive abilities facing decision problems without a stable top option. It recommends choices that maximize utility using quantizations of beliefs and desires in cases where probabilities and utilities (...) are indeterminate and using higher-order utility analysis in cases of limited access to probabilities and utilities. For agents burdened with mistakes, it advocates reasonable attempts to correct unacceptable mistakes before deciding. In decision problems without a stable top option, a topic of game theory, it proposes maximizing self-conditional utility among self-supporting options. In games of strategy, the new principles lead to solutions that are Pareto optimal among equilibria composed of jointly self-supporting strategies. Offering an account of bounded rationality, the bookmakes large strides toward realism in decision theory. (shrink)

My aim in this paper is to develop a unified solution to two paradoxes of bounded rationality. The first is the regress problem that incorporating cognitive bounds into models of rational decisionmaking generates a regress of higher-order decision problems. The second is the problem of rational irrationality: it sometimes seems rational for bounded agents to act irrationally on the basis of rational deliberation. I review two strategies which have been brought to bear on these problems: the way (...) of weakening which responds by weakening rational norms, and the way of indirection which responds by letting the rationality of behavior be determined by the rationality of the deliberative processes which produced it. Then I propose and defend a third way to confront the paradoxes: the way of level separation. (shrink)

Rationality has been a principle widely assumed in economics from early on. In contrast, rationality in the formation of economic expectations is rather new. Since the term “rational expectations” has meanwhile become a kind of slogan for diverse issues in economics as in related fields, there is some danger of authors’ not always being aware of the true meaning of this technical term. Sometimes it is used, in a context where expectation formation about uncertain events is essential, as (...) if rational expectations solved this fundamental problem. (shrink)

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line (...) of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems. (shrink)

When proponents of cognitive externalism (CE) turn to empirical studies in cognitive science to put the framework to use and to assess its explanatory success, they typically refer to perception, memory, or motor coordination. In contrast, not much has been said about reasoning. One promising avenue to explore in this respect is the theory of bounded rationality (BR). To clarify the relationship between CE and BR, we criticize Andy Clark's understanding of BR, as well as his claim that (...) BR does not fit his version of CE. We then propose and defend a version of CE—“scaffolded cognition”—that is not committed to constitutive claims about the mind, but still differs from mainstream internalism. Finally, we analyze BR from our own CE perspective, thereby clarifying its vague appeals to the environment, and argue that cognitive internalism cannot explain important aspects of the BR program. (shrink)

The notion of “bounded rationality” was introduced by Simon as an appropriate framework for explaining how agents reason and make decisions in accordance with their computational limitations and the characteristics of the environments in which they exist (seen metaphorically as two complementary scissor blades).We elaborate on how bounded rationality is usually conceived in psychology and on its relationship with logic. We focus on the relationship between heuristics and some non-monotonic logical systems. These two categories of cognitive (...) tools share fundamental features. As a step further, we show that in some cases heuristics themselves can be formalized from this logic perspective. We have therefore two main aims: on the one hand, to demonstrate the relationship between the bounded rationality programme and logic, understood in a broad sense; on the other hand, to provide logical tools of analysis of already known heuristics. This may lead to results such as the characterization of fast and frugal binary trees in terms of their associated logic program here provided. (shrink)