Bounded Rationality

A basic problem of organizational learning is recombining members' knowledge into innovative solutions. This article shows that extant approaches tend to position this problem in the organizational members' minds, thereby neglecting the problem of limited rationality. In some approaches of this type, organizational learning is conceptualized as the result of members learning from each other. In contrast to these concepts, some authors are convinced that a mode of organizational learning that is dependent on crosslearning between specialists cannot be effective because of the limits of an individual's cognitive abilities. These authors therefore try to identify organizational mechanisms that partly substitute for or support cross-learning between specialists. This article builds on this approach by developing a concept of 'transactive organizational learning', which takes organizational rules as repositories of knowledge and relies on organizational mechanisms that allow the direct transfer of specialists' knowledge into rules. Empirical support for this concept was found in case studies from two German companies.

The idea of libertarian paternalism might seem to be an oxymoron, but it is both possible and legitimate for private and public institutions to affect behavior while also respecting freedom of choice. Often people's preferences are ill-formed, and their choices will inevitably be influenced by default rules, framing effects, and starting points. In these circumstances, a form of paternalism cannot be avoided. Equipped with an understanding of behavioral findings of bounded rationality and bounded self-control, libertarian paternalists should attempt to steer people's choices in welfare-promoting directions without eliminating freedom of choice. It is also possible to show how a libertarian paternalist might select among the possible options and to assess how much choice to offer. Examples are given from many areas, including savings behavior, labor law, and consumer protection.

This paper reports an experiment that elicits subjects' initial responses to 16 dominance-solvable two-person guessing games. The structure is publicly announced except for varying payoff parameters, to which subjects are given free access. Varying the parameters allows very strong separation of the behavior implied by leading decision rules. Subjects' decisions and searches show that most subjects understood the games and sought to maximize payoffs, but many had simplified models of others' decisions that led to systematic deviations from equilibrium. The predictable component of their deviations is well explained by a structural nonequilibrium model of initial responses based on level-k thinking. (JEL C72, C92, D83)

This paper develops a theory of strategic group identity that explains how strategic groups emerge in an industry and how they can affect firm behaviors and outcomes. In so doing, it provides a theoretical basis for the existence of strategic groups. We argue that managers cognitively partition their industry environment to reduce uncertainty and to cope with bounded rationality. Social learning theory and social identification theory are used to describe how cognitive groups coalesce into meaningful substructures and how a group-level identity emerges. We describe the ways in which macro level factors condition the development of groups and their identities. We introduce the notion of a strong identity, which characterizes any group sufficiently recognized and attended to by members to affect individual action. Groups with 'weak identities' are no more than transient agglomerations of firms and do not exist in any meaningful sense. These ideas are developed into propositions that describe the conditions under which groups with strong identities are likely to emerge. A second set of propositions describes their transformation over time. Identity strength is linked to both positive and negative outcomes in a final set of propositions. We show how strategic groups with strong identities can affect firm performance, resolving a longstanding problem which has plagued strategic groups research and conclude by suggesting some approaches for measurement and future research. © 1997 by John Wiley & Sons, Ltd. led others to question even the existence of strategic groups, as distinct from random collections

This paper defines and analyzes a "sparse max" operator, which is a less than fully attentive and rational version of the traditional max operator. The agent builds (as economists do) a simplified model of the world which is sparse, considering only the variables of first-order importance. His stylized model and his resulting choices both derive from constrained optimization. Still, the sparse max remains tractable to compute. Moreover, the induced outcomes reflect basic psychological forces governing limited attention. The sparse max yields a behavioral version of two basic chapters of the microeconomics textbook: consumer demand and competitive equilibrium. We obtain a behavioral version of Marshallian and Hicksian demand, the Slutsky matrix, the Edgeworth box, Roy's identity etc. The Slutsky matrix is no longer symmetric: non-salient prices are associated with anomalously small demand elasticities. Because the consumer exhibits nominal illusion, in the Edgeworth box, the offer curve is a twodimensional surface rather than a one-dimensional curve. As a result, different aggregate price levels correspond to materially distinct competitive equilibria, in a similar spirit to a Phillips curve. This framework provides a way to assess which parts of basic microeconomics are robust, and which are not, to the assumption of perfect maximization.

This paper analyzes coalitions among self-interested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individually. A model of bounded rationality is adopted where computation resources are costly. It is not worthwhile solving the problems optimally: solution quality is decision-theoretically traded o against computation cost. A normative, application-and protocol-independent theory of coalitions among boundedrational agents is devised. The optimal coalition structure and its stability are signi cantly a ected by the agents' algorithms' performance pro les and the cost of computation. This relationship is rst analyzed theoretically. Then a domain classi cation including rational and bounded-rational agents is introduced. Experimental results are presented in vehicle routing with real data from ve dispatch centers. This problem is NP-complete and the instances are so large that|with current technology|any agent's rationality is bounded by computational complexity. 1 1 Automated negotiation systems with self-interested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructure|Internet, WWW, NII, EDI, KQML 8], FIPA, Telescript 14], Java, etc|over which separately designed agents belonging to di erent organizations can interact in an open environment in real-time and safely carry out transactions. The second reason is strong application pull for computer support for negotiation at the operative decision making level. For example, we are witnessing the advent of small transaction commerce on the Internet for purchasing goods, information, and communication bandwidth 21, 30]. There is also an industrial trend toward virtual enterprises: dynamic alliances of small, agile enterprises which together can take advantage of economies of scale when available (e.g., respond to more diverse orders than individual agents can), but do not su er from diseconomies of scale.

We consider several key aspects of prediction in language comprehension: its computational nature, the representational level(s) at which we predict, whether we use higher level representations to predictively pre-activate lower level representations, and whether we ‘commit’ in any way to our predictions, beyond pre-activation. We argue that the bulk of behavioral and neural evidence suggests that we predict probabilistically and at multiple levels and grains of representation. We also argue that we can, in principle, use higher level inferences to predictively pre-activate information at multiple lower representational levels. We also suggest that the degree and level of predictive pre-activation might be a function of the expected utility of prediction, which, in turn, may depend on comprehenders’ goals and their estimates of the relative reliability of their prior knowledge and the bottom-up input. Finally, we argue that all these properties of language understanding can be naturally explained and productively explored within a multi-representational hierarchical actively generative architecture whose goal is to infer the message intended by the producer, and in which predictions play a crucial role in explaining the bottom-up input.

Keywords: language comprehension, prediction error, generative model, probabilistic, surprisal

We estimate a dynamic asset pricing model characterized by heterogeneous boundedly rational agents. The fundamental value of the risky asset is publicly available to all agents, but they have different beliefs about the persistence of deviations of stock prices from the fundamental benchmark. An evolutionary selection mechanism based on relative past profits governs the dynamics of the fractions and switching of agents between different beliefs or forecasting strategies. A strategy attracts more agents if it performed relatively well in the recent past compared to other strategies. We estimate the model to annual US stock price data from 1871 until 2003. The estimation results support the existence of two expectation regimes, and a bootstrap F-test rejects linearity in favor of our nonlinear two-type heterogeneous agent model. One regime can be characterized as a fundamentalists regime, because agents believe in mean-reversion of stock prices toward the benchmark fundamental value. The second regime can be characterized as a chartist, trend following regime because agents expect the deviations from the fundamental to trend. The fractions of agents using the fundamentalists and trend following forecasting rules show substantial time variation and switching between predictors. The model offers an explanation for the recent stock prices run-up. Before the 1990s the trend following regime was active only occasionally. However, in the late 1990s the trend following regime persisted and reenforced an extraordinary deviation of stock prices from the ARTICLE IN PRESS www.elsevier.com/locate/jedc 0165-1889/$ -see front matter r

Information is a key resource for the new venture. Despite the importance EXECUTIVE of information search practices, little research has examined whether entre-SUMMARY preneurs show a tendency to search for more or less information under particular conditions. This article considers whether information search might be explained by concepts of bounded rationality. Such theories lead to the counterintuitive expectation that entrepreneurs with less experience or those entering unfamiliar fields would search less because of their more limited understanding of what is needed. In addition, entrepreneurs with higher levels of initial confidence would search less because their "entrepreneurial euphoria" may limit their ability to assess their own needs for additional information.

I n this introductory piece, we take stock of the impact of Cyert and March's A Behavioral Theory of the Firm, describe current research trends in the behavioral tradition, and introduce the special issue's papers. A Behavioral Theory of the Firm is one of the most influential management books of all time. In the book, Cyert and March developed theoretical building blocks that became the foundations for current research in organizational studies in management, economics, political science, and sociology. Cyert and March also made theoretical propositions that are investigated and extended in current work on organizational learning theory and evolutionary economics.

Risky health behaviors such as smoking, drinking alcohol, drug use, unprotected sex, and poor diets and sedentary lifestyles (leading to obesity) are a major source of preventable deaths. This chapter overviews the theoretical frameworks for, and empirical evidence on, the economics of risky health behaviors. It describes traditional economic approaches emphasizing utility maximization that, under certain assumptions, result in Pareto-optimal outcomes and a limited role for policy interventions. It also details nontraditional models (e.g. involving hyperbolic time discounting or bounded rationality) that even without market imperfections can result in suboptimal outcomes for which government intervention has greater potential to increase social welfare. The chapter summarizes the literature on the consequences of risky health behaviors for economic outcomes such as medical care costs, educational attainment, employment, wages, and crime. It also reviews the research on policies and strategies with the potential to modify risky health behaviors, such as taxes or subsidies, cash incentives, restrictions on purchase and use, providing information and restricting advertising. The chapter concludes with suggestions for future research.

Traditional fragmented and mechanistic science is unable to cope with issues about sustainability, as these are often related to complex, self-organizing systems. In the paper, sustainable development is seen as an unending process defined neither by fixed goals nor by specific means of achieving them. It is argued that, in order to understand the sources of and the solutions to modern problems, linear and mechanistic thinking must give way to non-linear and organic thinking, more commonly referred to as systems thinking. System Dynamics, which operates in a whole-system fashion, is put forward as a powerful methodology to deal with issues of sustainability. Examples of successful applications are given.

... A survey on the different functionalities in cognitive radio networks (also referred to as NeXt Generation (xG) networks) and the related research challenges were presented in [4]. Specifically, the key issues related to spectrum management, spectrum mobility, and spectrum ...

It has been repeatedly shown that in decisions under time constraints, individuals predominantly use noncompensatory strategies rather than complex compensatory ones. We argue that these findings might be due not to limitations of cognitive capacity but instead to limitations of information search imposed by the commonly used experimental tool Mouselab . We tested this assumption in three experiments. In the first experiment, information was openly presented, whereas in the second experiment the standard Mouselab program was used under different time limits. The results indicate that individuals are able to compute weighted additive decision strategies extremely quickly if information search is not restricted by the experimental procedure. In a third experiment, these results were replicated using more complex decision tasks, and the major alternative explanations that individuals use more complex heuristics or merely encode the constellation of cues were ruled out. In sum, the findings challenge the fundaments of bounded rationality and highlight the importance of automatic processes in decision making.

For a research program that counts improved empirical realism among its primary goals, it is surprising that behavioral economics appears indistinguishable from neoclassical economics in its reliance on 'as-if ' arguments. ' As-if ' arguments are frequently put forward in behavioral economics to justify 'psychological' models that add new parameters to fit decision outcome data rather than specifying more realistic or empirically supported psychological processes that genuinely explain these data. Another striking similarity is that both behavioral and neoclassical research programs refer to a common set of axiomatic norms without subjecting them to empirical investigation. Notably missing is investigation of whether people who deviate from axiomatic rationality face economically significant losses. Despite producing prolific documentation of deviations from neoclassical norms, behavioral economics has produced almost no evidence that deviations are correlated with lower earnings, lower happiness, impaired health, inaccurate beliefs, or shorter lives. We argue for an alternative non-axiomatic approach to normative analysis focused on veridical descriptions of decision process and a matching principle -between behavioral strategies and the environments in which they are used -referred to as ecological rationality. To make behavioral economics, or psychology and economics, a more rigorously empirical science will require less effort spent extending 'as-if ' utility theory to account for biases and deviations, and substantially more careful observation of successful decision makers in their respective domains. B nomics with «more realistic assumptions» is perhaps the guiding theme of behavioral economists, as behavioral economists undertake economic analysis without one or more of the unbounded rationality assumptions. These assumptions, which count among the defining elements of the neoclassical, or rational choice, model, are: unbounded self-interest, unbounded willpower, and unbounded computational capacity.

This article features an interdisciplinary debate and dialogue about the nature of mind, perception, and rationality. Scholars from a range of disciplines—cognitive science, applied and experimental psychology, behavioral economics, and biology—offer critiques and commentaries of a target article by Felin, Koenderink, and Krueger (2017): " Rationality, Perception, and the All-Seeing Eye, " Psychonomic Bulletin & Review. The commentaries raise a number of criticisms and issues concerning rationality and the all-seeing-eye argument, including the nature of judgment and reasoning, biases versus heuristics, organism–en-vironment relations, perception and situational construal, equilibrium analysis in economics, efficient markets, and the nature of empirical observation and the scientific method. The debated topics have far-reaching consequences for the rationality literature specifically, as well as for the cog-nitive, psychological, and economic sciences more broadly. The commentaries are followed by a response from the authors of the target article. Their response is organized around three central issues: (1) the problem of cues; (2) what is the question?; and (3) equilibria, $500 bills, and the axioms of rationality.

Since the mid-1970s, hundreds of academic studies have been conducted in risk perception-oriented research within the social sciences (e.g., nonfinancial areas) across various branches of learning. The academic foundation pertaining to the "psychological aspects" of risk perception studies in behavioral finance, accounting, and economics developed from the earlier works on risky behaviors and hazardous activities. This research on risky and hazardous situations was based on studies performed at Decision Research (an organization founded in 1976 by Paul Slovic) on risk perception documenting specific behavioral risk characteristics from psychology that can be applied within a financial and investment decision-making context. A notable theme within the risk perception literature is how an investor processes information and the various behavioral finance theories and issues that might influence a person's perception of risk within the judgment process. The different behavioral finance theories and concepts that influence an individual's perception of risk for different types of financial services and investment products are heuristics, overconfidence, prospect theory, loss aversion, representativeness, framing, anchoring, familiarity bias, perceived control, expert knowledge, affect (feelings), and worry. Ricciardi, Victor, The Psychology of Risk: The Behavioral Finance Perspective. HANDBOOK OF FINANCE: VOLUME 2: INVESTMENT MANAGEMENT AND FINANCIAL MANAGEMENT, Frank J. Fabozzi, ed., John Wiley & Sons, pp. 85-111, 2008.

Keywords: risk, perception, risk perception, perceived risk, judgment, decision making, behavioral decision theory (BDT) , behavioral accounting, standard finance, behavioral finance, behavioral economics, psychology, efficient market hypothesis, rationality, bounded rationality, classical decision theory

In recent years, understanding the structure and function of complex networks has become the foundation for explaining many different real-world complex biological, technological and informal social phenomena. Techniques from statistical physics have been successfully applied to the analysis of these networks, and have uncovered surprising statistical structural properties that have also been shown to have a major effect on their functionality, dynamics, robustness, and fragility. This paper examines, for the first time, the statistical properties of strategically important organizational networks - networks of people engaged in distributed product development (PD) - and discusses the significance of these properties in providing insight into ways of improving the strategic and operational decision-making of the organization. We show that the structure of information flow networks that are at the heart of large-scale product development efforts have properties that are similar to those displayed by other social, biological and technological networks. In this context, we identify novel properties that may be characteristic of other information-carrying networks. We further present a detailed model and analysis of PD dynamics on complex networks, and show how the underlying network topologies provide direct information about the characteristics of this dynamics. We believe that our new analysis methodology and empirical results are also relevant to other organizational information-carrying networks.

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

During the past decade, the use of conditional cash transfer programs to increase investment in human capital has generated considerable excitement in both research and policy forums. This article surveys the existing literature, which suggests that most conditional cash transfer programs are used for essentially one of two purposes: restoring efficiency when externalities exist or improving equity by targeting resources to poor households. The programs often meet their stated objectives, but in some instances there is tension between the efficiency and equity objectives. The overall impact of a program depends on the gains and losses associated with each objective.

Seeing—perception and vision—is implicitly the fundamental building block of the literature on rationality and cognition. Herbert Simon and Daniel Kahneman's arguments against the omniscience of economic agents—and the concept of bounded rationality—depend critically on a particular view of the nature of perception and vision. We propose that this framework of rationality merely replaces economic omniscience with perceptual omniscience. We show how the cognitive and social sciences feature a pervasive but problematic meta-assumption that is characterized by an Ball-seeing eye.^ We raise concerns about this assumption and discuss different ways in which the all-seeing eye manifests itself in existing research on (bounded) rationality. We first consider the centrality of vision and perception in Simon's pioneering work. We then point to Kahneman's work—particularly his article BMaps of Bounded Rationality^—to illustrate the per-vasiveness of an all-seeing view of perception, as manifested in the extensive use of visual examples and illusions. Similar assumptions about perception can be found across a large literature in the cognitive sciences. The central problem is the present emphasis on inverse optics—the objective nature of objects and environments, e.g., size, contrast, and color. This framework ignores the nature of the organism and per-ceiver. We argue instead that reality is constructed and expressed, and we discuss the species-specificity of perception , as well as perception as a user interface. We draw on vision science as well as the arts to develop an alternative understanding of rationality in the cognitive and social sciences. We conclude with a discussion of the implications of our arguments for the rationality and decision-making literature in cognitive psychology and behavioral economics, along with suggesting some ways forward.

The relation between knowledge and action is discussed. It is argued that knowledge comprises static as well as dynamic aspects that are related to expectation of success of possible action and control over the performance of factual action, respectively. Both views concur in success of action as common point of reference. The main claim of this paper is that regarding this reference to action propositional knowledge does not diiffer from practical knowledge. In this way action establishes a natural dependency of knowledge on context. The approach is compared to the analytical characterization of propositional knowledge as justified true belief, pointing at existing connections. Consequences for knowledge management are indicated regarding the support of knowledge intensive work and knowledge transfer.

Under the assumption of bounded rationality, economic agents learn from their past mistaken predictions by combining new and old information to form new beliefs. The purpose of this paper is to investigate how the policy-maker, by affecting private agents' learning process, determines the speed at which the economy converges to the rational expectation equilibrium. I find that by reacting strongly to private agents' expected inflation, a central bank increases the speed of convergence and shortens the length of the transition to the rational expectation equilibrium. I use speed of convergence as an additional criterion for evaluating alternative monetary policies. I find that a fast convergence is not always desirable.

A person learns by trial and error if he occasionally tries out new strategies, rejecting choices that are erroneous in the sense that they do not lead to higher payoffs. In a game, however, strategies can become erroneous due to a change of behavior by someone else. We introduce a learning rule in which behavior is conditional on whether a player experiences an error of the first or second type. This rule, called interactive trial and error learning, implements Nash equilibrium behavior in any game with generic payoffs and at least one pure Nash equilibrium.

An evolutionary perspective on policies to foster sustainable development is presented. It is argued that policies suggested by the traditional economic theory of environmental policy can stimulate unsustainable socio-economic structures and patterns. In addition, they are unable to remove undesired locked-in systems and technologies. Drawing on evolutionary thinking, characterised by diversity, selection, innovation, path-dependence and bounded rationality, an alternative, partly complementary theory of environmental policy is suggested. Specific attention is given to the role of strategies that are aimed at increasing diversity and adaptive flexibility, and at reducing risk. D

This paper reviews experience with two programs sponsored by the US Environmental Protection Agency * the Green Lights and Energy Star O$ce Products programs * that promote the adoption of energy-e$cient technologies through voluntary agreements with private sector "rms. The evidence suggests that Green Lights has induced "rms to make investments in cost-saving lighting systems that "rms failed to exploit prior to the program's implementation. Energy Star O$ce Products, in contrast, has led suppliers of computers and electronic equipment to substantially improve the energy e$ciency of their products in ways that confer net cost savings on equipment users. The paper argues that the success of these programs is based on their ability to reduce market failures related to problems of imperfect information and bounded rationality that impair the e!ectiveness of both intra-"rm organization and the coordination between equipment suppliers and their customers. Given the nature of the technologies in question, these programs should have little e!ect on the demand for energy services so that energy e$ciency improvements should lead to one-to-one reductions in the level of energy use.

Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we propose an information-theoretic formalization of bounded rational decision-making where decision-makers trade off expected utility and information processing costs. Such bounded rational decision-makers can be thought of as thermodynamic machines that undergo physical state changes when they compute. Their behavior is governed by a free energy functional that trades off changes in internal energy-as a proxy for utility-and entropic changes representing computational costs induced by changing states. As a result, the bounded rational decision-making problem can be rephrased in terms of well-known concepts from statistical physics. In the limit when computational costs are ignored, the maximum expected utility principle is recovered. We discuss the relation to satisficing decision-making procedures as well as links to existing theoretical frameworks and human decision-making experiments that describe deviations from expected utility theory. Since most of the mathematical machinery can be borrowed from statistical physics, the main contribution is to axiomatically derive and interpret the thermodynamic free energy as a model of bounded rational decision-making.

This paper presents the concept and theoretical specification of a multi-agent based model for spatio-temporal simulation of a coupled human–landscape system. The model falls into the class of all agents, where the human population and the landscape environment are all self-organized interactive agents. The model framework is represented by four components: (i) a system of human population defining specific behavioural patterns of farm households in land-use decision-making according to typological livelihood groups, (ii) a system of landscape environment characterising individual land patches with multiple attributes, representing the dynamics of crop and forest yields as well as land-use/cover transitions in response to household behaviour and natural constraints, (iii) a set of policy factors that are important for land-use choices, and (iv) a decision-making procedure integrating household, environmental and policy information into land-use decisions of household agents. In the model, the bounded-rational approach, based on utility maximisation using spatial multi-nominal logistic functions, is nested with heuristic rule-based techniques to represent decision-making mechanisms of households regarding land use. Empirical verifications of the model's components and the application of the model to a watershed in Vietnam for integrated assessments of policy impacts on landscape and community dynamics are subjects of a companion paper.

Many producers and consumers of legacy training simulator and game environments are beginning to envision a new era where psych-socio-physiologic models could be interoperated to enhance their environments' simulation of human agents. This article explores whether we could embed our behavior modeling framework (described in Part I) behind a legacy first person shooter 3-D game environment to recreate portions of the Black Hawk Down scenario. Section One amplifies on the inter-operability needs and challenges confronting the field, presents the questions that are examined, and describes the test scenario. Sections 2 and 3 review the software and knowledge engineering methodology, respectively, needed to create the system and populate it with bots. Results (Section 4) and discussion (Section 5) reveal that we were able to generate plausible and adaptive recreations of Somalian crowds, militia, women acting as shields, suicide bombers, and more. Also, there are specific lessons learned about ways to advance the field so that such inter-operabilities will become more affordable and widespread.

Under the rubric of internalization theory, transaction cost-based arguments have been dominant in addressing multinational firms' mode of foreign market entry decisions. Another line of argument addresses this decision by focusing more closely on firms' capabilities. This paper ...

This paper outlines a relational theory of risk. According to this theory, risk emerges from situated cognition that establishes a relationship of risk between a risk object and an object at risk, so that the risk object is considered, under certain contingent circumstances and in some causal way, to threaten the valued object at risk. This relational theory of risk is a theory about the interpretative nature of risk that answers the key theoretical and practical questions of why and how something is considered a risk. The relational theory of risk allows for the interpretation of risk situations as culturally informed, and thereby suggests new ways to approach risk communication, risk governance, and risk management by taking into account bounded rationalities of thought and action.

The classical view that equates rationality with adherence to the laws of probability theory and logic has driven much research on inference. Recently, an increasing number of researchers have begun to espouse a view of rationality that takes account of organisms' adaptive goals, natural environments, and cognitive constraints. We argue that inference is carried out using boundedly rational heuristics, that is, heuristics that allow organisms to reach their goals under conditions of limited time, information, and computational capacity. These heuristics are ecologically rational in that they exploit aspects of both the physical and social environment in order to make adaptive inferences. We review recent work exploring this multifaceted conception of rationality.

Intuition can lead to more effective decision-making than analysis under certain conditions. This assumption can be regarded as common sense. However, dominant research streams on intuition effectiveness in decision-making conceptualize intuition inadequately, because intuition is considered either detrimental or as a form of analysis. Current findings in general intuition research show that intuition is a holistic form of information processing that is distinct from analysis and can be superior in some cases. To reconcile this mismatch, this article first critically assesses dominant conceptions on intuition effectiveness and then offers a re-conceptualization that builds on current findings of general intuition research. Basically, the article suggests the structuredness of the decision problem as the main criterion for intuition effectiveness, and proposes organization information processing theory to establish this link conceptually. It is not the uncertainty but the equivocality of decision problems that call for an intuitive approach. The article conclusively derives implications for further research and discusses potential restrictions and constraints.