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gracias a la teoría de los juegos tenemos una mejor comprensión del comportamiento humano en la economía. Sin embargo, como esta teoría excluye el aspecto psicológico de la conducta, una revisión del supuesto de racionalidad completa la información perdida en algunos juegos. Como consecuencia, han surgido algunos enfoques que incluyen aspectos conductuales y psicológicos en los juegos. Esto ha generado una gran cantidad de literatura distribuida en líneas de investigación aparentemente independientes, hecho que puede generar confusión. Para aclarar si la teoría de juegos conductual y psicológica son enfoques independientes, se realizó una revisión sistemática utilizando las directrices PRISMA para identificar todos los estudios empíricos publicados bajo ambas denominaciones. Se recogieron trabajos que (1) tuvieran variables psicológicas, (2) estuvieran revisados por pares y (3) tuvieran algún diseño experimental. De los 492 trabajos buscados, 67 se incluyeron en esta revisión sistemática. Se organizarwon y estudiaron para determinar qué tipo de variables psicológicas incluían y si realmente existen dos enfoques diferentes o no. El término más utilizado es la teoría del juego conductual, en la que se utilizan ampliamente variables como la culpa, la confianza, la motivación y la reciprocidad. La principal conclusión es que los dos enfoques son realmente el mismo y son los seguidores de los principales autores de cada corriente los que publican bajo uno u otro nombre.//thanks to the game theory we have a better understanding of human behavior in the economy. However, since this theory excludes the psychological aspect from conduct, a revision of the rationality assumption completes the missed information in some games. As a consequence, some approaches have emerged including behavioral and psychological aspects in games. This has generated a large amount of literature distributed in apparently independent lines of research, a fact that could generate confusion. To clarify whether behavioral game theory and psychological game theory are independent approaches, a systematic review was conducted using the PRISMA guidelines to identify all empirical studies published under both names. Papers that (1) had psychological variables, (2) were peer-reviewed, and (3) had any experimental design were collected. From 492 papers searched, 67 were included in this systematic review. They were organized and studied to determine what type of psychological variables they included and whether there are really two different approaches. The most common term used is behavioral game theory in which variables like guilt, trust, motivation, and reciprocity are widely used. The main conclusion is that the two approaches are really the same and it is the followers of the main authors of each current who publish under one or the other name.

PurposeIn 1951, Kenneth Arrow proved that it is not possible to have a group decision-making procedure that satisfies reasonable requirements like fairness. From the theoretical viewpoint, this is a great result – well-deserving the Nobel Prize that was awarded to Professor Arrow. However, from the practical viewpoint, the question remains – so how should we make group decisions? A usual way to solve this problem is to provide some reasonable heuristic ideas, but the problem is that different seemingly reasonable idea often lead to different group decision – this is known, e.g. for different voting schemes.Design/methodology/approachIn this paper we analyze this problem from the viewpoint of decision theory, the basic theory underlying all our activities – including economic ones.FindingsWe show how from the first-principles decision theory, we can extract explicit recommendations for group decision making.Originality/valueMost of the resulting recommendations have been proposed earlier. The main novelty of this paper is that it provides a unified coherent narrative that leads from the fundamental first principles to practical recommendations.

Multi-attribute group decision-making (MAGDM) is one of the most active research fields in modern social cognition and decision science theory. An obvious challenge in MAGDM problems is that it is not easy to appropriately describe decision-makers’ (DMs’) cognitive information, which is because the cognition of DMs is usually diverse and contains a lot of uncertainties and fuzziness. The recently proposed q-rung dual hesitant fuzzy set (q-RDHFS), encompassing diverse cognition and providing wide information space, has been proved to be effective to depict DMs’ cognitive information in the MAGDM process. However, existing operations and aggregation operators of q-RDHFSs still have limitations, which result in the weakness of existing q-RDHFS-based MAGDM methods. Therefore, this paper aims at introducing new operational rules and aggregation operators to deal with q-rung dual hesitant fuzzy information. To this end, we first propose a wide range of generalized operations for q-rung dual hesitant fuzzy elements (q-RDHFEs) based on Archimedean t-norm and t-conorm. Second, we put forward some new aggregation operators by generalizing the recently invented extended power average (EPA) operator into q-RDHFSs. Existing literature has revealed the powerfulness and flexibility of the EPA operator over the classical power average operator. Finally, a new MAGDM approach based on the proposed operators is developed. Our proposed method can effectively handle MAGDM problems with q-rung dual hesitant fuzzy cognitive information. Some numerical examples are conducted to demonstrate the validity of the new MAGDM method. Further, we conduct parameter analysis and comparative analysis to prove the flexibility and superiority of our proposed MAGDM method, respectively. In a word, this paper contributes to a new q-rung dual hesitant fuzzy MAGDM method, which absorbs the advantages of EPA operator and Archimedean operations. This method can be applied to describe complex cognitive information and solving realistic MAGDM problems effectively.

This thesis consists of four papers on ethics and decision theory. Although the papers are closely related, each is written to make a standalone contribution. So, the thesis should not be read as one continuous project. Ignoring the Improbable. Many problems in decision theory appear to be solvable if agents simply ignore some possibilities. The utility of this approach has given rise to a substantial number of theories endorsing discounting: ignoring states whose probability is below a particular threshold. This paper argues that ignoring possibilities—even extremely remote ones—comes at a hefty cost for one’s ability to make rational decisions. First, the approach is inescapably partition-sensitive: agents will undertake different acts depending on how the world is described. Second, agents become insensitive to differences in the probability of excluded states; they will be indifferent between taking a small risk and a much smaller risk. Third, agents become insensitive to differences in the value of outcomes in excluded states; they will be indifferent between risking a bad outcome and the same risk of a much worse outcome. And fourth, excluding a state affects the expected value of all acts to which the state is relevant, generating implausible prescriptions for peripheral acts; for instance, agents will not take any bet on the excluded state since they have assigned it a probability of zero. Superiority Discounting Implies the Preposterous Conclusion. Many population axiologies avoid the Repugnant Conclusion (RC) by endorsing Superiority: Some number of great lives is better than any number of mediocre lives. But as Nebel shows, RC follows (given plausible auxiliary assumptions) from the Intrapersonal Repugnant Conclusion (IRC): A guaranteed mediocre life is better than a sufficiently small probability of a great life. This result is concerning because IRC is plausible. Recently, Kosonen has argued that IRC can be true while RC is false if small probabilities are discounted to zero. This paper details the unique problems created by combining Superiority with discounting. The resultant view, Superiority Discounting, avoids the Repugnant Conclusion only at the cost of the Preposterous Conclusion: Near-certain hell for arbitrarily many people is better than near-certain heaven for arbitrarily many people. Filtered Maximization. According to moral absolutism, consequentialist considerations may justify sufficiently small risks of violating a duty but not sufficiently large risks. But finding a value function that accommodates these preferences is notoriously difficult: Seemingly, if some amount of consequentialist value outweighs a small risk, then some larger amount should be able to outweigh a larger risk. Critics have taken this difficulty to warrant rejection of absolutism generally; others have attempted to solve the problem by offering decision-theoretic models of absolutist decision making. I outline five desiderata that such a model must satisfy and demonstrate that none of the leading four theories satisfy all five. Then, I present an alternative: Filtered Maximization. This theory models absolutists as assigning “duty value” to outcomes and filtering out acts whose expected duty value is sufficiently low. Then, of those remaining, agents perform the act that maximizes expected value. I illustrate how Filtered Maximization satisfies all five desiderata, then conclude by discussing some implications for existing absolutist theories. Superiority and Separability. Superiority is the view that there exists some pair of valuable objects x and y such that some quantity of x is better than any quantity of y; it is very plausible when x is an important good and y is trivial, such as in the Repugnant Conclusion. This paper shows that (given modest auxiliary assumptions) Superiority is incompatible with Separability—the principle that in comparing the value of two outcomes, we may ignore people whose welfare and existence are unaffected.

Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary-choice judgments, also known as classification tasks—for example, yes/no or build/buy decisions—social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially, inaccurate groups become increasingly inaccurate after information exchange, even as they signal stronger support. We term this dynamic the “crowd classification problem.” Using both a novel data set and a reanalysis of three previous data sets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary-choice vote can become less accurate, even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence. This paper was accepted by Lamar Pierce, organizations.

The topic of this paper is the foundation for individual rights proposed by David Gauthier in his seminal 1986 book Morals by Agreement, and particularly the role of conception of rationality in this foundation. The foundation of rights is a part of Gauthier’s broader enterprise: to ground morals in rationality – more specifically, in the economic conception of rationality. Because of the importance of this conception for the whole of Gauthier’s project, we reconstruct first the conception of rationality which can be found in decision theory and game theory, presenting simultaneously in a relatively non-technical way some basic concepts of the aforementioned disciplines. We proceed then to reconstruction of the foundation of rights itself – it turns on Gauthier’s interpretation of the so-called “Lockean proviso.” Lastly, we turn to the connection between rationality and foundation of rights. It is to be found in the narrow compliance – the disposition to enter only into cooperation which satisfies conditions of fairness set out in part by the Lockean proviso.

Identifying critical uncertainties about ecological systems can help prioritize research efforts intended to inform management decisions. However, exclusively focusing on the ecological system neglects the objectives of natural resource managers and the associated social values tied to risks and rewards of actions.I demonstrate how to prioritize research efforts for a harvested population by applying expected value of perfect information (EVPI) to harvest decisions made with a density‐independent matrix population model. Research priorities identified by EVPI diverge from priorities identified by matrix elasticity analyses that ignore social utility.Using a density‐dependent harvest model, the value of information about the intrinsic productivity of a population is shown to be sensitive to the socially determined penalty for implementing a harvest rate that deviates from the goal because of imperfection in estimation. Synthesis and applications. The effect of including social values into harvest decision‐making depends on the assumed population model, uncertainty in population vital rates, and the particular form of the utility function used to represent risk/reward of harvest. EVPI analyses that include perceived utility of different outcomes can be used by managers seeking to optimize monitoring and research spending. Collaboration between applied ecologists and social scientists that quantitatively measure peoples' values is needed in many structured decision‐making processes., Decisions about natural resource management can be informed by ecological science and human perception of risks and rewards. This paper describes analytical methods for making decisions about animal harvest that integrate ecological and social science.

PurposeBoundary decision is an important but underexplored theme in digital platform research. The boundary decision of digital platform enterprises (DPEs) differs from traditional organizations because of cross-side network effects (CNEs). This study intends to investigate whether transaction cost economics (TCE) and resource-based view (RBV), as classical organization boundary mechanisms, are still applicable for DPEs.Design/methodology/approachTo unfold the research problem, this study conducts a fuzzy-set qualitative comparative analysis (fsQCA) on the samples of 21 platform business units.FindingsThe results show that the classical boundary decision theory still applies in the context of DPEs, but the cross-side network effects will affect boundary decision of DPEs.Originality/valueThis study provides a new framework – integrates TCE, RBV and CNEs – to analyze boundary decision of DPEs. This paper also contributes to research on both organization boundary decision and platform governance.

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix.

Hesitant fuzzy cognitive information provides an effective and convenient form for expressing human subjective cognition about the research object, i.e., hesitant fuzzy sets (HFSs). Studies on decision making with HFSs have become an important branch in decision theory, in which operational laws of hesitant fuzzy elements (HFEs) play a core role in the solution. However, current HFE computational laws have the disadvantages of subjectivity and dimensional problems. Therefore, how to define an objective HFE operational rule without dimensional problems is an open issue. This paper introduces an idempotent HFE computing rule to overcome current disadvantages. The weighted mean of HFEs under the developed computing rule is further discussed. In addition, a novel comparison law between HFEs is proposed. The property of idempotence is introduced to provide an intuitive integrated result of HFEs. To decrease the integrated HFEs dimensions, the sliding window model is utilized. Fundamental mathematical properties of the developed operations are discussed. Furthermore, the normal weighted means of HFEs are extended by using the developed idempotent computing rules. Finally, a novel comparison law for comparing HFEs is designed, which is further used to provide a multiattribute decision procedure. Additive idempotent is developed as a special and intuitive property for the HFE additive operation. Normal weighted means of HFEs, including arithmetic and geometric means, are correspondingly derived. Numerical examples have shown that the proposed HFE operational laws are valid, which can effectively decrease the dimensions of integrated results. The developed idempotent computing rules provide a novel HFE algebra structure, which includes the additive operation, multiplicative operation, scalar multiplication and power operation. By using the sliding window model, the developed idempotent computing rules can effectively reduce the integrated HFEs dimensions. The strength of the developed computational model is that integrating two identical pieces of cognitive information produces the same result. In addition, the modified HFE comparison law can overcome the drawback of current comparison laws, and a much more reasonable comparison result can be obtained.

Decision sciences are in general agreement on the theoretical relevance of decision training. From an empirical standpoint, however, only a few studies test its effectiveness or practical usefulness, and even less address the impact of decision training on the structuring of problems systematically. Yet that task is widely considered to be the most crucial in decision-making processes, and current research suggests that effectively structuring problems and generating alternatives—as epitomized by the concept of proactive decision making—increases satisfaction with the decision as well as life satisfaction more generally. This paper empirically tests the effect of decision training on two facets of proactive decision making—cognitive skills and personality traits—and on decision satisfaction. In quasi-experimental field studies based on three distinct decision-making courses and two control groups, we analyze longitudinal data on 1,013 decision makers/analysts with different levels of experience. The results reveal positive training effects on proactive cognitive skills and decision satisfaction, but we find no effect on proactive personality traits and mostly non-significant interactions between training and experience. These results imply the practical relevance of decision training as a means to promote effective decision making even by more experienced decision makers. The findings presented here may be helpful for operations research scholars who advocate for specific instruction concerning proactive cognitive skills in courses dedicated to decision quality and/or decision theory and also for increasing, in such courses, participants’ proactive decision making and decision satisfaction. Our results should also promote more positive decision outcomes.

In this paper, we first provide a framework for history-dependent utility models. We further consider discrete infinite horizon dynamic optimization programs in which the instantaneous payoff presents such historydependence. Habits are a particular case of history-dependence. An issue about models with habits is their lack of general framework. Our framework allows to study habit models that are either additive or multiplicative or neither, as well as satiation models. Moreover, with this unified setting one can treat the usual optimal growth models with or without habit formation and with or without satiation effects. As the way the history dependence is formalized allows us to use dynamic programming tools. We show that the value function is the unique fixed point of the Bellman operator. Such history-dependent modelizations have their motivations and applications in many areas among which decision theory, psychology, behavioral and environmental economics.

Invasive species can lead to community-level damage to the invaded ecosystem and extinction of native species. Most surveillance systems for the detection of invasive species are developed based on expert assessment, inherently coming with a level of uncertainty. In this research, info-gap decision theory (IGDT) is applied to model and manage such uncertainty. Surveillance of the Asian House Gecko, Hemidactylus frenatus Duméril and Bibron, 1836 on Barrow Island, is used as a case study. Our research provides a novel method for applying IGDT to determine the population threshold ($$K$$ K ) so that the decision can be robust to the deep uncertainty present in model parameters. We further robust-optimize surveillance costs rather than minimize surveillance costs. We demonstrate that increasing the population threshold for detection increases both robustness to the errors in the model parameter estimates, and opportuneness to lower surveillance costs than the accepted maximum budget. This paper provides guidance for decision makers to balance robustness and required surveillance expenditure. IGDT offers a novel method to model and manage the uncertainty prevalent in biodiversity conservation practices and modelling. The method outlined here can be used to design robust surveillance systems for invasive species in a wider context, and to better tackle uncertainty in protection of biodiversity and native species in a cost-effective manner.

Most philosophical decision theorists and philosophers of the social sciences believe that decision theory is and should be in the business of providing folk psychological explanations of choice behaviour, and that it can only do so if we understand the preferences, utilities and probabilities that feature in decision-theoretic models as ascriptions of mental states not reducible to choice. The behavioural interpretation of preference and related concepts, still common in economics, is consequently cast as misguided. This paper argues that even those who strive to provide folk psychological explanations should side with the economists, and adopt a behavioural interpretation of the preferences featuring in decision-theoretic models. Under a mentalistic inter-pretation of preference, decision-theoretic models do not straightforwardly provide ordinary folk psychological explanations. Instead, they involve controversial enough commitments about the mental causes of choice to not only fail to adequately capture much unreflective decision-making, but also many intentional, reason-based and instrumentally rational choices. Satisfactory folk psychological explanation in fact only comes indirectly from inferring more fundamental conative attitudes from a pattern of decision-theoretic preferences. And the behavioural interpretation does a better job at facilitating such inferences. My argument extends to the related concepts of utility and probability.

The maturity and commercialization of emerging digital technologies represented by artificial intelligence, cloud computing, block chain and virtual reality are giving birth to a new and higher economic form, that is, digital economy. Digital economy is different from the traditional industrial economy. It is clean, efficient, green and recyclable. It represents and promotes the future direction of global economic development, especially in the context of the sudden COVID-19 pandemic as a continuing disaster. Therefore, it is essential to establish the comprehensive evaluation model of digital economy development scientifically and reasonably. In this paper, first on the basis of literature analysis, the relevant indicators of digital economy development are collected manually and then screened by the grey dynamic clustering and rough set reduction theory. The evaluation index system of digital economy development is constructed from four dimensions: digital innovation impetus support, digital infrastructure construction support, national economic environment and digital policy guarantee, digital integration and application. Next the subjective weight and objective weight are calculated by the group FAHP method, entropy method and improved CRITIC method, and the combined weight is integrated with the thought of maximum variance. The grey correlation analysis and improved VIKOR model are combined to systematically evaluate the digital economy development level of 31 provinces and cities in China from 2013 to 2019. The results of empirical analysis show that the overall development of China’s digital economy shows a trend of superposition and rise, and the development of digital economy in the four major economic zones is unbalanced. Finally, we put forward targeted opinions on the construction of China’s provincial digital economy.

The conditional distributivity of aggregation operations, that is distributivity observed under additional condition imposed on the domain of aggregation operations, is an important property that is highly useful in various areas, such as the decision making theory and the integration theory. Therefore, the topic of this paper is this relaxed form of distributivity for continuous semi-t-operators with respect to disjunctive uninorms with continuous underlying t-norms and t-conorms.

In this paper we review how the uncertainty in spatial information has been characterized. This includes both continuous predictions of spatial variables, and thematic maps of landcover classes. We contend that much work in this area has failed to engage adequately with the decision processes of the end-user of information, and that the engagement of spatial statisticians is essential to achieve this. We examine generalized measures of uncertainty, and those focussed on particular decision models. We conclude that the latter are likely to be the most fruitful, particularly if they emerge from a formal decision analysis. We outline the principles of value of information theory, and suggest that this represents an ideal framework in which to develop measures of uncertainty which can support both the rational collection of data and the interpretation of the resulting information.