The idea that there is a fundamental difference in value between persons and things, and that respecting this difference is an important moral requirement, has strong intuitive appeal. Kantian ethics is unique in placing this requirement at the center of a moral system and in explicating the conditions for complying with it. Unlike challenges to Kantian ethics that focus on tragic cases that pit respect for one person against respect for another, this paper focuses on the question of how we (...) can respect the value distinction between persons and things under conditions of uncertainty. After exploring why decision making under uncertainty is a neglected topic among Kantians and demonstrating how uncertainty challenges our ability to comply with this norm, we propose a notion of morally insignificant risk within a framework that allows agents to navigate real-world decisions involving material benefit and some risk to dignity without violating the Kantian’s core commitments. We conclude by exploring some of the challenges facing this approach. (shrink)

Both economists and psychologists are interested in understanding decision making under uncertainty. Yet, they rely on different concepts to analyse human behaviour: economists use economic preference parameters rooted in utility theory, while psychologists use personality traits to describe responses to uncertain situations. Using a large sample of university students, this study examines and contrasts five economic preference parameters and six psychological personality traits that are commonly used to study individuals’ attitudes towards uncertainty. A novelty of (...) this paper is including both the economic concept of ambiguity aversion as well as the personality trait of ambiguity intolerance. We find that standard economic preference measures based on incentivized choice tasks seem to capture rather different characteristics than psychological personality traits. In contrast, economic preference measures obtained from self-assessment questions appear more related to personality traits, especially ambiguity intolerance. (shrink)

This article is an attempt at a systematic account of decision making under greater uncertainty than what traditional, mathematically oriented decision theory can cope with. Four components of great uncertainty are distinguished: (1) the identity of the options is not well determined (uncertainty of demarcation) ; (2) the consequences of at least some option are unknown (uncertainty of consequences); (3) it is not clear whether information obtained from others, such as experts, can (...) be relied on (uncertainty of reliance); and (4) the values relevant for the decision are not determined with sufficient precision (uncertainty of values). Some possible strategy types are proposed for each of these components. Decisions related to environmental issues are used to illustrate the proposals. (shrink)

This paper is a first attempt to study the problem of aggregation of individual ordinal probabilistic beliefs in an Arrowian framework. We exhibit some properties an aggregation rule must fulfil; in particular we prove the existence of a “quasi-dictator”.

The disjunction effect (Tversky & Shafir, 1992) occurs when decision makers prefer option x (versus y) when knowing that event A occurs and also when knowing that event A does not occur, but they refuse x (or prefer y) when not knowing whether or not A occurs. This form of incoherence violates Savage's (1954) sure-thing principle, one of the basic axioms of the rational theory of decision making. The phenomenon was attributed to a lack of clear reasons (...) for accepting an option (x) when subjects are under uncertainty. Through a pragmatic analysis of the task and a consequent reformulation of it, we show that the effect does not depend on the presence of uncertainty, but on the introduction of non-relevant goals into the text problem, in both the well-known Gamble problem and the Hawaii problem. (shrink)

This article is an attempt at a systematic account of decision making under greater uncertainty than what traditional, mathematically oriented decision theory can cope with. Four components of great uncertainty are distinguished: (1) the identity of the options is not well determined (uncertainty of demarcation) ; (2) the consequences of at least some option are unknown (uncertainty of consequences); (3) it is not clear whether information obtained from others, such as experts, can (...) be relied on (uncertainty of reliance); and (4) the values relevant for the decision are not determined with sufficient precision (uncertainty of values). Some possible strategy types are proposed for each of these components. Decisions related to environmental issues are used to illustrate the proposals. (shrink)

Stress and anxiety can both influence risk-taking in decision-making. While stress typically increases risk-taking, anxiety often leads to risk-averse choices. Few studies have examined both stress and anxiety in a single paradigm to assess risk-averse choices. We therefore set out to examine emotional decision-making under stress in socially anxious participants. In our study, individuals (N= 87) high or low in social anxiety completed an expanded variation of theBalloon Analogue Risk Task(BART). While inflating a balloon to (...) a larger degree is rewarded, a possible explosion leads to (a) a loss of money and (b) it is followed by an emotional picture (i.e., a calm vs. an angry face). To induce stress before this task, participants were told that they would have to deliver a speech. We operationalized risk-taking by the number of pumps during inflation and its functionality by the amount of monetary gain. In addition, response times were recorded as an index of decisional conflict. Without the stressor, high socially anxious compared to low socially anxious participants did not differ in any of the dependent variables. However, under stress, the low socially anxious group took more risk and earned more money, while high socially anxious individuals remained more cautious and did not change their risk-taking under social stress. Overall, high socially anxious individuals made their decisions more hesitantly compared to low socially anxious individuals. Unexpectedly, there were no main effects or interactions with the valence of the emotional faces. This data shows that stress affects socially anxious individuals differently: in low socially anxious individuals stress fosters risk-taking, whereas high socially anxious individuals did not alter their behavior and remained risk-averse. The noveleBARTis a promising research tool to examine the specific factors that influence decision-making. (shrink)

Decisions are made under uncertainty when there are distinct outcomes of a given action, and one is uncertain to which the act will lead. Decisions are made under indeterminacy when there are distinct outcomes of a given action, and it is indeterminate to which the act will lead. This paper develops a theory of (synchronic and diachronic) decision-making under indeterminacy that portrays the rational response to such situations as inconstant. Rational agents have to capriciously (...) and randomly choose how to resolve the indeterminacy relevant to a given choice-situation, but such capricious choices once made constrain how they will choose in the future. The account is illustrated by the case of self-interested action in situations where it is indeterminate whether you yourself will survive to benefit or suffer the consequences. The conclusion emphasizes some distinctive anti-hedging predictions of the account. (shrink)

We typically have to act under uncertainty. We can be uncertain about the relevant descriptive facts, but also about the relevant normative facts. However, the search for a theory of decision-making under normative uncertainty is doomed to failure. First, the most natural proposal for what to do given normative uncertainty faces two devastating problems. Second, the motivations for wanting a theory of what to do given descriptive uncertainty do not carry over to (...) normative uncertainty. Descriptive facts may be inaccessible even in principle, and ignorance of them excuses one from blame, but normative facts are in principle accessible, and ignorance of them arguably is no excuse. Normative facts differ from descriptive facts: normative facts affect what we ought to do no matter what, while descriptive facts affect what we ought to do only insofar as we are aware of them. (shrink)

It is a commonplace that in making decisions agents often have to juggle competing values, and that no choice will maximise satisfaction of them all. However, the prevailing account of these cases assumes that there is always a single ranking of the agent's values, and therefore no unresolvable conflict between them. Isaac Levi denies this assumption, arguing that agents often must choose without having balanced their different values and that to be rational, an act does not have to be (...) optimal, only what Levi terms 'admissible'. This book explores the consequences of denying the assumption and develops a general approach to decision-making under unresolved conflict. Professor Levi discusses conflicts of value in several domains - those arising in moral dilemmas, the drawing of scientific inferences, decisions taken under uncertainty, and in social choice. In each of these he adapts his theoretical framework, showing how conflict may often be reduced though not always altogether eliminated. (shrink)

Emotions influence human decisions under risk and uncertainty, even when they are unrelated to the decisions, i.e. incidental to them. Empirical findings are mixed regarding the directions and sizes of the effects of discrete emotions such as fear, anger, or happiness. According to the Appraisal-Tendency Framework (ATF), appraisals of certainty and control determine why same-valence emotions can differentially alter preferences for risky and uncertain options. Building upon this framework of emotion-specific appraisals, we conducted a systematic review and meta-analysis (...) of 28 experimental studies on the effects of discrete incidental emotions on decision-making under risk and uncertainty. We evaluated potential moderators at the task and study levels. We find emotion-specific, moderately heterogeneous effects partially in line with the expectations of the ATF. The framing and financial consequences of choices, the type of choices, and the presence of other participants during the task do not moderate the effect. Our meta-analytic results support the differential influence of discrete, incidental emotions on decision-making under risk and uncertainty depending on appraisals other than valence. We discuss limited sample sizes and heterogeneity as reasons for the absence of significant moderators and encourage experimental investigations of individual differences in the susceptibility to incidental affective influences. (shrink)

Underscoring the economic significance of the Knightian distinction between risk and uncertainty, Don Katzner forcefully challenges the continued dominance of the expected utility model based on subjective probability in macroeconomic analysis and offers in its place a simple yet elegant model of decision making inspired by the pioneering work of G.L.S. Shackle. In doing so, Katzner lends support to a research program to identify a more coherent and empirically grounded theory of decision making under (...) uncertainty. Our paper makes three contributions to this program. First, we argue that the appropriate choice of a model of individual behavior under uncertainty cannot be adjudicated merely on logical, a priori grounds, but must ultimately be based on consistency with observed behavior. There are many internally consistent models and their external conditions of validity hardly ever rely on inconceivable scenarios. Second, while Katzner’s model certainly provides a plausible foundation for understanding choice under uncertainty, we suggest that it is somewhat underdetermined. It requires further theoretical articulation to yield testable predictions which differentiate it from competing approaches. Third, we discuss recent experimental findings that may need to be addressed by any satisfactory theoretical account of decision making under uncertainty. (shrink)

Professor Dreze is a highly respected mathematical economist and econometrician. This book brings together some of his major contributions to the economic theory of decision making under uncertainty, and also several essays. These include an important essay on 'Decision theory under moral hazard and state dependent preferences' that significantly extends modern theory, and which provides rigorous foundations for subsequent chapters. Topics covered within the theory include decision theory, market allocation and prices, consumer decisions, (...) theory of the firm, labour contracts, and public decisions. (shrink)

Recent work in judgment and decisionmaking has stressed that institutions, like individuals, often rely on decisionmaking heuristics. But most of the institutional decisionmaking heuristics studied to date are highly firm- and industry-specific. This contrasts to the individual case, in which many heuristics are general-purpose rules suitable for a wide range of decision problems. Are there also general-purpose heuristics for institutional decisionmaking? In this paper, I argue that a number of methods recently developed for decisionmaking under deep uncertainty (...) have a good claim to be understood as general-purpose decisionmaking heuristics suitable for a broad range of institutional decision problems. (shrink)

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction betweendirect quantitative uncertaintyandindirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative (...) uncertainty describes the quality of knowledge concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty. (shrink)

. Decision-making under uncertainty is sometimes investigated as a homogeneous problem, independently of the type of decision-maker and the level and nature of the decision itself. However, when the decision-maker is a public authority, there immediately arise problems additional to those that concern any other decision-maker. This is not always clearly recognised in orthodox discussions on decisions under conditions of uncertainty. This article investigates the methodological, strategic and procedural challenges of (...) taking public decisions in such conditions. It focuses mainly on decisions involving urban contexts, such as planning decisions regarding land use and building transformations, by trying to develop some pioneering research studies in this field. (shrink)

How do people make difficult decisions in situations involving substantial risk and uncertainty? In this study, we presented a difficult medical decision to three expert physicians in a combined “thinking aloud” and “cross examination” experiment. Verbatim transcripts were analyzed using script analysis to observe the process of constructing and making the decision, and using referring phrase analysis to determine the representation of knowledge of likelihoods. These analyses are compared with a formal decision analysis of the (...) same problem to highlight similarities and differences. The process of making the decision resembles an incremental, sequential‐refinement planning algorithm, where a complex decision is broken into a sequence of choices to be made with a simplified description of the alternatives. This strategy results in certain kinds of relevant information being underweighted in the final decision. Knowledge of likelihood appears to be represented as symbolic descriptions capturing categorical and ordinal relations with “landrhark” likelihoods, only some of which are described numerically. Numerical probabilities, capable of being combined and compared arithmetically, were not observed. These observations suggest an explanation for the heuristics and biases in human decision making under uncertainty in terms of the processes that manipulate symbolic descriptions of likelihoods and construct plans of action for situations involving risk and uncertainty. (shrink)

This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when (...) a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

It is important to have an adequate model of uncertainty, since decisions must be made before the uncertainty can be resolved. For instance, flood defenses must be designed before we know the future distribution of flood events. It is standardly assumed that probability theory offers the best model of uncertain information. I think there are reasons to be sceptical of this claim. I criticise some arguments for the claim that probability theory is the only adequate model of (...) class='Hi'>uncertainty. In particular I critique Dutch book arguments, representation theorems, and accuracy based arguments. Then I put forward my preferred model: imprecise probabilities. These are sets of probability measures. I offer several motivations for this model of uncertain belief, and suggest a number of interpretations of the framework. I also defend the model against some criticisms, including the so-called problem of dilation. I apply this framework to decision problems in the abstract. I discuss some decision rules from the literature including Levi’s E-admissibility and the more permissive rule favoured by Walley, among others. I then point towards some applications to climate decisions. My conclusions are largely negative: decision making under such severe uncertainty is inevitably difficult. I finish with a case study of scientific uncertainty. Climate modellers attempt to offer probabilistic forecasts of future climate change. There is reason to be sceptical that the model probabilities offered really do reflect the chances of future climate change, at least at regional scales and long lead times. Indeed, scientific uncertainty is multi-dimensional, and difficult to quantify. I argue that probability theory is not an adequate representation of the kinds of severe uncertainty that arise in some areas in science. I claim that this requires that we look for a better framework for modelling uncertainty. (shrink)

The processes underlying motor decision-making have recently caught considerable amount of scientific attention, focusing on the integration of empirical evidence from sensorimotor control research with psychological theories and computational models on decision-making. Empirical studies on motor decision-making suggest that the kinematics of goal-directed reaching movements are sensitive to the level of target uncertainty during movement planning. However, the source of uncertainty as a relevant factor influencing the process of motor decision-making (...) has not been sufficiently considered, yet. In this study, we test the assumption that the source of target uncertainty has an effect on motor decision-making, which can be proven by analyzing movement variability during the time course of movement execution. Ten healthy young adults performed three blocks with 66 trials of goal-directed reaching movements in each block, across which the source and level of reach target uncertainty at movement onset were manipulated (“no uncertainty”, “extrinsic uncertainty”, “intrinsic uncertainty”). Fingertip position of the right index finger was recorded using an optical motion tracking system. Standard kinematic measures (i.e. path length and movement duration) as well as variability of fingertip position across the time course of movement execution and at movement end were analyzed. In line with previous studies, we found that a high level of extrinsic target uncertainty leads to increased overall movement duration, which could be attributed to increased path length in this condition, as compared to intrinsic and no target uncertainty (all p <.001). Movement duration and path length did not show any differences between the latter two conditions. However, the time course analysis of movement variability revealed significant differences between these two conditions, with increased variability of fingertip position in the presence of intrinsic target uncertainty (Condition × Sampling point: p =.01), though considerably less than under high extrinsic target uncertainty (p ≤.001). These findings suggest that both the level and source of uncertainty have a significant effect on the processing of potential action plans during motor decision-making, which can be revealed through the analysis of the time course of movement variability at the end-effector level. (shrink)

ABSTRACTThis paper examines how decisions to limit treatment to critically ill patients under uncertainty can be made rationally. Expected utility theory offers one way of making rational decisions under uncertainty. One problem with using this approach is that we may not know the value of each option. One rational course open is to treat until further information becomes available. However, treatment can limit the range of options open. With treatment, a patient may recover such that (...) he no longer requires life‐supporting treatment. However, his life may be not worth living. If active euthanasia of %on‐terminal’conditions is prohibited, the option of dying will no longer be available. Taking a rational‘wait and see’course may result in being trapped within an unbearable life. On the other hand, sometimes present practice‘lets nature takes its course'. Critically ill patients are allowed to die because it is believed that their lives will be not worth living. It is likely that some patients are allowed to die when there is some objective chance of worthwhile future life. This paper argues that a policy of treating critically ill patients until the nature of future options can be better evaluated, in company with an offer of subsequent euthanasia where appropriate, allows a more rational and humane approach to treatment limitation decisions under uncertainty. (shrink)

Current environmental problems and technological risks are a challenge for a new institutional arrangement of the value spheres of Science, Politics and Morality. Distinguished authors from different European countries and America provide a cross-disciplinary perspective on the problems of political decision making under the conditions of scientific uncertainty. cases from biotechnology and the environmental sciences are discussed. The papers collected for this volume address the following themes: (i) controversies about risks and political decision making; (...) (ii) concepts of science for policy; (iii) the use of social science in the policy making process; (iv) ethical problems with developments in science and technology; (v) public and state interests in the development and control of technology. (shrink)

Conviction Narrative Theory (CNT) is conceptually so multifaceted as to make critical evaluation difficult. It also omits one course of action: Active engagement with the world. Parsing the developmental and mechanistic processes within CNT would allow for a rigorous research programme to put the account under test. I propose a unifying account based on active inference.

While Conviction Narrative Theory correctly criticizes utility-based accounts of decision-making, it unfairly reduces probabilistic models to point estimates and treats affect and narrative as mechanistically opaque yet explanatorily sufficient modules. Hierarchically nested Bayesian accounts offer a mechanistically explicit and parsimonious alternative incorporating affect into a single biologically plausible precision-weighted mechanism that tunes decision-making toward narrative versus sensory dependence under varying uncertainty levels.

Innovative research on decision making under ‘deep uncertainty’ is underway in applied fields such as engineering and operational research, largely outside the view of normative theorists grounded in decision theory. Applied methods and tools for decision support under deep uncertainty go beyond standard decision theory in the attention that they give to the structuring of decisions. Decision structuring is an important part of a broader philosophy of managing uncertainty in (...) decision making, and normative decision theorists can both learn from, and contribute to, the growing deep uncertainty decision support literature. (shrink)

This paper proposes several concepts of efficient solutions for multicriteria decision problems under uncertainty. We show how alternative notions of efficiency may be grounded on different decision ‘contexts’, depending on what is known about the Decision Maker's (DM) preference structure and probabilistic anticipations. We define efficient sets arising naturally from polar decision contexts. We investigate these sets from the points of view of their relative inclusions and point out some particular subsets which may be (...) especially relevant to some decision situations. (shrink)

This is a very short textbook on probabilistic reasoning, expected utility decision-making, cognitive biases, and self-correction, especially in application to medical examples. It also includes a chapter on concepts of health.

Multiple measures of decision-making under uncertainty (e.g. jumping to conclusions (JTC), bias against disconfirmatory evidence (BADE), win-switch behavior, random exploration) have been associated with delusional thinking in independent studies. Yet, it is unknown whether these variables explain shared or unique variance in delusional thinking, and whether these relationships are specific to paranoia or delusional ideation more broadly. Additionally, the underlying computational mechanisms require further investigation. To investigate these questions, task and self-report data were collected in 88 (...) individuals (46 healthy controls, 42 schizophrenia-spectrum) and included measures of cognitive biases and behavior on probabilistic reversal learning and explore/exploit tasks. Of those, only win-switch rate significantly differed between groups. In regression, reversal learning performance, random exploration, and poor evidence integration during BADE showed significant, independent associations with paranoia. Only self-reported JTC was associated with delusional ideation, controlling for paranoia. Computational parameters increased the proportion of variance explained in paranoia. Overall, decision-making influenced by strong volatility and variability is specifically associated with paranoia, whereas self-reported hasty decision-making is specifically associated with other themes of delusional ideation. These aspects of decision-making under uncertainty may therefore represent distinct cognitive processes that, together, have the potential to worsen delusional thinking across the psychosis spectrum. (shrink)

Recent research indicates that angry facial expressions are preferentially processed and may facilitate automatic avoidance response, especially in socially anxious individuals. However, few studies have examined whether this bias also expresses itself in more complex cognitive processes and behavior such as decision making. We recently introduced a variation of the Iowa Gambling Task which allowed us to document the influence of task-irrelevant emotional cues on rational decision making. The present study used a modified gambling task to (...) investigate the impact of angry facial expressions on decision making in 38 individuals with a wide range of social anxiety. Participants were to find out which choices were (dis-) advantageous to maximize overall gain. To create a decision conflict between approach of reward and avoidance of fear-relevant angry faces, advantageous choices were associated with angry facial expressions, whereas disadvantageous choices were associated with happy facial expressions. Results indicated that higher social avoidance predicted less advantageous decisions in the beginning of the task, i.e., when contingencies were still uncertain. Interactions with specific skin conductance responses further clarified that this initial avoidance only occurred in combination with elevated responses before choosing an angry facial expressions. In addition, an interaction between high trait anxiety and elevated responses to early losses predicted faster learning of an advantageous strategy. These effects were independent of intelligence, general risky decision-making, self-reported state anxiety, and depression. Thus, socially avoidant individuals who respond emotionally to angry facial expressions are more likely to show avoidance of these faces under uncertainty. This novel laboratory paradigm may be an appropriate analog for central features of social anxiety. (shrink)

This study examines how heightened expectations of board responsibility and accountability affect the socio-cognitive decision-making of boards and their collective task performance. Using data from the directors of 60 boards who served before and after the enactment of Sarbanes–Oxley, this study provides insight into the potential negative impact that this tightened accountability environment can have on a board’s task performance. Examining several socio-cognitive elements of board decision-making, board authority is found to have a positive main effect (...) on board task performance, while relative CEO power and affective conflict have curvilinear relationships with board task performance. Cohesiveness also moderates the relationship between a board’s perceived uncertainty and affective conflict with board task performance. In sum, the model shows how a new era of director accountability can affect the social cognitions of board decision-making that underlie board task performance. (shrink)

Belief formation and justification of belief is the subject of epistemology, philosophy of mind, and philosophy of action. In this article we are mostly interested in the application of analytic techniques for the explication of belief justification under uncertainty. We need to explicate this phenomenon in order to answer, at least in part, the question of what are the features of reasoning made in conditions that cause doubts, how people make decisions in such conditions. Arguments used for the (...) justification of such decisions have the status of plausible arguments. The crucial issues related to the analysis and evaluation of plausible arguments are of the acceptability of the premises and the transmission of their acceptability to the conclusion. In this article, we have focused on the transition from the premises to the conclusions of plausible arguments, on the transmission of justification of premises to the conclusions. To establish the peculiarities of such a transmission an outline of the semantics for such arguments is proposed. Its key component is the measures of the plausibility of the premises and rules of inference. A plausible argument itself does not provide the ultimate reason for accepting its conclusion. The justification of the conclusion also depends on other arguments that support or defeat it. So to establish the degree of justification of the conclusion we need to attribute the weights to the premises and rules of inference. We hope that this study provides at least a preliminary answer to the question of how the failure of the transmission of justification in plausible arguments differs from the failure of transmission in deductive arguments. (shrink)

Analyzing situations where information is partial, incomplete or contradictory has created a demand for quantitative belief measures that are weaker than classic probability theory. In this paper, we compare two frameworks that have been proposed for this task, Dempster-Shafer theory and non-standard probability theory based on Belnap-Dunn logic. We show the two frameworks to assume orthogonal perspectives on informational shortcomings, but also provide a partial correspondence result. Lastly, we also compare various dynamical rules of the two frameworks, all seen as (...) generalizations of classic Bayes’ conditioning. (shrink)

Regulatory science, which generates knowledge relevant for regulatory decision?making, is different from standard academic science in that it is oriented mainly towards the attainment of non?epistemic (practical) aims. The role of uncertainty and the limits to the relevance of academic science are being recognized more and more explicitly in regulatory decision?making. This has led to the introduction of regulation?specific scientific methodologies in order to generate decision?relevant data. However, recent practical experience with such non?standard methodologies (...) indicates that they, too, may be subject to important limitations. We argue that the attainment of non?epistemic values and aims (like the protection of human health and the environment) requires not only control of the quality of the data and the methodologies, but also the selection of the level of regulation deemed adequate in each specific case (including a decision about which of the two, under?regulation or over?regulation, would be more acceptable). (shrink)

The increasing success of the evidence-based policy movement is raising the demand of empirically informed decision making. As arguably any policy decision happens under conditions of uncertainty, following our best available evidence to reduce the uncertainty seems a requirement of good decision making. However, not all the uncertainty faced by decision makers can be resolved by evidence. In this paper, we build on a philosophical analysis of uncertainty to identify (...) the boundaries of scientific advice in policy decision making. We start by introducing a distinction between empirical and non-empirical types of uncertainty, and we explore the role of two non-empirical uncertainties in the context of policy making. We argue that the authority of scientific advisors is limited to empirical uncertainty and cannot extend beyond it. While the appeal of evidence-based policy rests on a view of scientific advice as limited to empirical uncertainty, in practice there is a risk of over reliance on experts beyond the legitimate scope of their authority. We conclude by applying our framework to a real-world case of evidence-based policy, where experts have overstepped their boundaries by ignoring non-empirical types of uncertainty. (shrink)

Mainstream theories of decision making conceptualise uncertainty in terms of a well-defined probability distribution or weighting function. Following Knight, radical Keynesians consider subjective expected utility (SEU) theory and its variants as a restricted theory of decision-making applicable to situations of risk and, hence, of limited relevance to the understanding of crucial economic decisions under conditions of fundamental uncertainty in which probabilities are ill-defined, possibly non-existent. The objective of this paper is to outline a (...) radical Keynesian theory of decision-making under uncertainty, arguing that Keynes's suggestion to a two-dimensional probability-credence framework provides the basis for determining the limitations of mainstream approaches and points the way forward to the construction of a more general encompassing theory relevant to psychologists and economists outside of the Keynesian tradition. (shrink)

Rational decision making depends on what one believes, what one desires, and what one knows. In conventional decision models, beliefs are represented by probabilities and desires are represented by utilities. Software agents are knowledgeable entities capable of managing their own set of beliefs and desires, and they can decide upon the next operation to execute autonomously. They are also interactive entities capable of filtering communications and managing dialogues. Knowledgeability includes representing knowledge about the external world, reasoning with (...) it, and sharing it. Interactions include negotiations to perform tasks in cooperative, coordinative, and competitive ways. In this paper we focus on decision-making mechanisms for agent-based systems on the basis of agent interaction. We identify possible interaction scenarios and define mechanisms for decision making in uncertain environments. It is believed that software agents will become the underlying technology that offers the capability of distribution of competence, control, and information for the next generation of ubiquitous, distributed, and heterogeneous information systems. (shrink)