Academic literature on the topic 'Decision making theories'

We describe the observable content of some of the most widely used models of decision under uncertainty: models of translation invariant preferences. In particular, we characterize the models of variational, maxmin, constant absolute risk aversion, and constant relative risk aversion utilities. In each case we present a revealed preference axiom that is satisfied by a dataset if and only if the dataset is consistent with the corresponding utility representation. We test our axioms using data from an experiment on financial decisions.

The decisions employers make are of critical importance to sociological understandings of labor market stratification. While contemporary research documents employment outcomes with ever-growing precision, far less work examines how employers actually make decisions. In this article, I review research on the process of employer decision making, focusing on how employers evaluate, compare, and select workers in personnel decisions. I begin by summarizing the most prevalent theories of employer decision making in sociology, grouping them into competency-based, status-based, and social closure–based approaches. A common thread underlying much of this work is the assumption that employers are utility maximizers who base decisions on systematic, even if flawed, cognitive calculations of worker skill and workforce productivity. I then turn to recent research from sociology and beyond that challenges this notion and highlights the importance of understanding how employers themselves—their emotions, identities, and environments—affect decisions. I conclude by suggesting directions for future research.

Abstract. There is growing evidence that decisions made on behalf of other people differ from the decisions we make for ourselves because we are less affected by the subjective experience of their outcome. As a result, the decisions we make for other people can be more optimal. This experiment investigated surrogate decision making using a probability discounting task where participants made choices between risky and sure options. Psychological distance between the decision maker and the recipient was manipulated by having participants make decisions for themselves, their friend, and another unknown participant. Risk preferences were closer to neutrality (i.e., more consistent with expected value) when making decisions on behalf of another participant than when making decisions for themselves or a friend. We conclude that subjective risk preferences are attenuated in surrogate decision making. Findings are discussed in relation to inconsistencies in the literature and theories of surrogate decision making.

AbstractNormative theories can be useful in developing descriptive theories, as when normative subjective expected utility theory is used to develop descriptive rational choice theory and behavioral game theory. “Ought” questions are also the essence of theories of moral reasoning, a domain of higher mental processing that could not survive without normative considerations.

How should we model learning behaviour in economic agents? This thesis addresses this question in two distinct ways. In the first set of chapters the assumption is that agents learn through the observation of others. They use Bayesian updating which together with specific informational assumptions can generate the problem known as herding with the potential for significant welfare losses. In the final set of chapters the agent is instead modelled as learning by example. Here the agent cannot learn by observing others, but has a pool of experience to fall back on. This allows us to examine how an economic agent will perform if he sees a particular economic situation (or game) for the first time, but has experience of playing related games. The tool used to capture the notion of learning through example is a neural network. Throughout the thesis the central theme is that economic agents will naturally use as much information as they can to help them make decisions. In many cases this should mean they take into consideration others' actions or their own experiences in similar but non-identical situations. Learning throughout the thesis will be rational or bounded-rational in the sense that either the best possible way to learn will be utilized (so players achieve full rational play, for example, through Bayesian updating), or a suitable local error-minimizing algorithm will be developed (for example, a rule of thumb which optimizes play in a subclass of games, but not in the overall set of possible games). Several themes permeate the whole thesis, including the scope for firms or planners to manipulate the information that is used by agents for their own ends, the role of rules of thumb, and the realism of current theories of learning in economics.

Researchers have increasingly directed attention to the importance of emotions in decision making. Recent theories have focused on the interpersonal effects of emotions--the influence of the decision maker's expressed emotions on observers' decisions and judgments. In the current research, we examine people's expectations of how incidental, discrete emotions affect behavior. We also study how these expectations affect decisions in interactive settings, and contrast them with how emotions actually impact other people's behavior.These ideas were tested in four studies. In Study 1a, participants (N = 58) answered a questionnaire asking their perceptions of how different emotions affect behavior. In Study 1b, participants (N = 203) read a number of hypothetical scenarios in which different interactions between them and another person took place. Studies 2 (N = 98) and 3 (N = 132) were two economic games -- a Stag-Hunt game and a Trust Game -- involving decisions with non-trivial financial consequences.Across these four studies, I found that people do have strong beliefs about how incidental emotions affect behaviors. Because of these beliefs, when told about their counterparts' emotional state, people in interactive settings modify their behavior. The impact of people's beliefs on behavior, however, was more consistent for negative emotions such as anger and fear, than for positive emotions such as happiness and gratitude. These findings also indicate that people are sensitive to the different effects of different emotions: different negative emotions such as guilt and anger have different effects on their expectations. Finally, I found that people's expectations about how their counterparts' emotions affect behavior can be inaccurate in specific settings.

The idea of a United Nations (UN) intelligence system is widely misunderstood and debates about it seem to be both misplaced and anecdotal. The lack of a consistent theory on intelligence has fostered the widely held view that such a system is not feasible or incompatible with the UN collective security system. This dissertation takes as its central thesis, the question, of whether an intelligence system is both desirable and feasible within the UN structure. In spite of the fact that no known study has so far engaged with the subject matter at the depth presented in this dissertation, the study advances the concept of collective intelligence, and its implications for managing international conflicts. The dissertation examines existing barriers in efforts to interface intelligence system with the UN structure, and proposes that, with suitable refinements, the concept of intelligence need not be incompatible with the UN system. It is also argued that these constraints should not preclude evolutionary changes to include an intelligence system that is compatible with an organisation such as the UN. By developing a concept of collective intelligence, the thesis proposes theoretical frameworks that suggest a potential nature of a viable intelligence capability within the UN. The analysis is developed normatively and conceptually, which lead to a further conclusion that the UN already possesses an intelligence capability which exists in manner that is not recognised. The lack of scholarly efforts to ground such a system on a reasonable framework creates a vacuum in the study of international organisations, and in particular the United Nations system. At a minimum, this dissertation fills this gap.

The complexity of financial analysis, particularly on selection process or decision making problems, has increased rapidly over several decades. As a result, much attention has been focused on developing and implementing the efficient mathematical models for supporting this kind of problems. Multiple criteria decision analysis, an advanced field of operations research provides analysts or decision makers a broad range of methodologies, which are all suited to the complexity of financial decision analysis. In the financial modeling, uncertainty problems are inevitable, owing to the fact that the consequences of events are not precisely known. In addition, human judgments as part of analysis also contribute to it intricacy. Correspondingly, many studies have been concentrated on integrating uncertainty theories in modeling the real financial problems. One area of interest is on the inclusion of the element of human behavior or attitudinal character of decision makers. Aggregation operator in this case can offer a wide spectrum of analysis or flexibility in modeling the human behavior in financial decision analysis. In general, the main purpose of this work is on the study of financial selection problems from the perspective of decision analysis, uncertainty theories and aggregation operators. To be specific, the decision problems under a finite or discrete case and multidimensional factors are studied. The emphasis is given on the group decision making models, notably, the Dempster-Shafer theory (DST) of belief structure, the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS). Moreover, the uncertainty theories based on fuzzy set theory and imprecise probability are employed, together with information fusion based on the ordered weighted average (OWA) operators. Quantitative and qualitative preferences, decision strategies based on the attitudinal character of decision makers, and majority concepts for group consensus are highlighted. The specific contributions of this work are summarized as the following: • The first contribution is on developing the multi-expert multi-criteria decision making (ME-MCDM) model with respect to two-stage aggregation processes. In specific, the aggregation of criteria is based on the integration of weighted arithmetic mean (WA) and OWA. The main attention is given on the proposed alternative OWAWA operator as an extension of immediate WA and OWAWA operators. Two approaches for modeling the majority opinion of experts are studied, in which based on the induced OWA (IOWA) operators. Some modifications to the support functions are suggested as to derive the order inducing variables. The analysis of ME-MCDM model based on these aggregation processes then is conducted. In this study the selection of investment strategy is used as to exemplify the model. • The weighted-selective aggregated majority-OWA operator may be considered as the second contribution. It is as an extension of the SAM-OWA operator, where the reliability of information sources is considered. The WSAM-OWA then is generalized to the quantified WSAM-OWA by incorporating the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM-IOWA with an ordering step is proposed for the individual fusion strategy. These aggregation operators are then implemented to the case of alternative scheme of heterogeneous group decision analysis, in particular for a selection of investment problem. • Third contribution is represented by the development of linguistic group decision making with Dempster-Shafer belief structure. Different type of linguistic aggregation operator such as the 2-tuple induced linguistic OWA operator is suggested. Specifically, it is based on order-inducing variables in which the ordering of the arguments and uncertain situations can be assessed with linguistic information. Then, by using the 2-TILOWA in the D-S framework, the belief structure-2-TILOWA operator can be formed. Some of its main properties are studied. This model is applied in a selection of financial strategies. • The extension of AHP for group decision making model is given as the fourth contribution, notably, based on the inclusion of IOWA operators. Two-stage aggregation processes used in the AHP-GDM model are extended. Firstly, a generalization of weighted maximal entropy OWA under the IOWA operator is proposed as to aggregate the criteria. Further, the majority concept based on the IOWA and Minkowski OWA-based similarity measure is suggested to determine a consensus among experts. This model provides a variant of decision strategies for analyzing the individual and the majority of experts. The application in investment selection problem is presented to test the reliability of the model. • The fifth contribution is on the integration of heavy ordered weighted geometric (HOWG) aggregation operators in AHP-GDM model. In the sense of heavy OWA operator (HOWA), the heavy weighted geometric (HWG) and HOWG are introduced as extensions of the normal weighted geometric mean (WG) and the OWG by relaxing the constraints on the associated weighting vector. These HWG and HOWG operators then are utilized in the aggregation process of AHP-GDM, specifically on the aggregation of individual judgments procedure. The main advantage of the model, besides the complete overlapping of information such in classical methods, is that it can also accommodate partial and non-overlapping information in the formulation. An investment selection problem is applied to demonstrate the model. • The extension of TOPSIS for group decision making model by the inclusion of majority concept may be considered as the sixth contribution. The majority concept is derived based on the induced generalized OWA (IGOWA) operators. Two fusion schemes in TOPSIS model are designed. First, an external fusion scheme to aggregate the experts’ judgments with respect to the concept of majority opinion on each criterion is suggested. Then, an internal fusion scheme of ideal and anti-ideal solutions that represents the majority of experts is proposed using the Minkowski OWA distance measures. The comparison of the proposed model with some other TOPSIS models with respect to distance measures is presented. Here, a general case of selection problem is presented, specifically on the human resource selection problem. • Finally, the group decision making model based on conflicting bifuzzy sets (CBFS) is proposed. Precisely, the subjective judgments of experts, mainly from positive and negative aspects are considered simultaneously in the analysis. Moreover, the weighting method for the attribute (or sub-attribute) is subject to the integration of subjective and objective weights. The synthesis of CBFS in the model is naturally done by extending the fuzzy evaluation in parallel with the intuitionistic fuzzy set. A new technique to compute the similarity measure is proposed, in which, being the degree of agreement between the experts. The model then is applied in the case study of flood control project selection problem. To sum up, the presented thesis dealt with the extension of multi-criteria decision analysis models for the financial selection problems (as a specific scope) and also the general selection problems with the inclusion of attitudinal character, majority concept and fuzzy set theory. In particular, the group decision making model, Dempster-Shafer belief structure, AHP and TOPSIS are proposed to overcome the shortcoming of the existing models, i.e., related to the financial decision analysis. The applicability and robustness of the developed models have been demonstrated and some sensitivity analyses are also provided. The main advantages of the proposed models are to provide a more general and flexible models for a wider analysis of the decision problems.
La tesis, a través del análisis y desarrollo del Análisis de decisiones, Teorías de incertidumbre y Operadores de agregación, busca contribuir al estado del arte y nuevas propuesta de las necesidades y demandas que los decisores, responsables o e inversores financieros se encuentran por la creciente complejidad de sus análisis y estrategias, sobre todo en los procesos de selección o en los problemas de decisión. Así, el objetivo principal de esta tesis es el estudio de los problemas de selección financiera desde la perspectiva del análisis de decisiones, las teorías de la incertidumbre y los operadores de agregación. En concreto, se estudian los problemas de decisión en virtud de un conjunto finito de alternativas (caso discreto) y de factores multidimensionales. En el trabajo se desarrolla una extensión de los modelos de análisis de decisiones multicriterio y multiexperto que se utilizan en la resolución de los problemas de selección financiera (como ámbito específico), pero también en los problemas de selección generales, con la inclusión del carácter actitudinal, el concepto de mayoría y la teoría de los conjuntos borrosos. En particular, el énfasis se sitúa en los modelos de toma de decisiones en grupo y en la estructura de creencias Dempster-Shafer (D-S), el proceso analítico jerárquico (AHP) i la técnica de orden de preferencia por similitud con la solución ideal (TOPSIS). Además, se aplican las teorías de incertidumbre basadas en conjuntos borrosos y de probabilidades imprecisas juntamente con la fusión de la información basada en operadores OWA. También se destaca las preferencias cuantitativas y cualitativas, las estrategias de decisión basadas en el carácter actitudinal de los decisores, y el concepto de mayoría en el consenso grupal, de forma que se propone el desarrollo de operadores OWA, la generalización de los modelos AHP y TOPSIS, juntamente con el modelo de toma de decisiones grupal y la estructura de creencias Dempster-Shafer, con el fin de superar las deficiencias de los modelos existentes en relación con el análisis de decisiones financieras. En particular, la investigación realizada se puede sintetizar en siete aportaciones específicas al state-of-the-art del Análisis de decisiones y los operadores de agregación, con aplicaciones en diferentes problemas de decisión financiera: 1. Operadores de agregación basados en los OWA en los modelos de decisión Multiexpertos y Multicriterio. 2. Operadores ponderados SAM-OWA y su aplicación en modelos GDM con operadores lingüísticos. 3. Modelos GDM con operadores lingüísticos adaptados a la teoría de Dempster-Shafer con la aplicación de operadores de agregación inducidos lingüísticos. 4. Generalización del modelo AHP para decisiones grupales usando operadores OWA inducidos. 5. Introducción de operadores OWA geométricos y pesados en los modelos GDM y AHP. 6. Ampliación de los modelos TOPSIS con operadores de agregación basados en los OWA. 7. Desarrollo y aplicación del Conflicting bifuzzy a modelos de decisión MAGDM En la tesis se demuestra la aplicabilidad y la robustez de los modelos desarrollados, tanto con un esquema de agregación de expertos clásicos como con un esquema alternativo que separa por criterios de decisión. Las principales ventajas de los modelos propuestos son que se tratan modelos más generales y flexibles para un análisis más amplio de los problemas de decisión, en particular de los de selección financiera, que incorporen diversos criterios, expertos y componentes de incertidumbre y lingüísticos.

The past 40 years have seen an enormous amount of research aimed at investigating human reasoning and decision-making abilities. This research has led to an extended debate about the extent to which humans meet the standards of normative theories of rationality. Recently, it has been proposed that dual-process theories, which posit that there are two distinct types of cognitive systems, offer a way to resolve this debate over human rationality. I will propose that the two systems of dual-process theories are best understood as investigative kinds. I will then examine recent empirical research from the cognitive neuroscience of decision-making that lends empirical support to the theoretical claims of dual-process theorists. I will lastly argue that dual-process theories not only offer an explanation for much of the conflicting data seen in decision-making and reasoning research, but that they ultimately offer reason to be optimistic about the prospects of human rationality.

This learning brief provides illustrative examples of three major adaptations the Coalition for Religious Equality and Inclusive Development (CREID) programme has undertaken while working within fragile contexts to promote freedom of religion or beilief (FoRB). These examples highlight how the programme has utilised embedded monitoring, evaluation, and learning (MEL) strategies to encourage what Ramalingam et al. (2019) refer to as ‘adaptive rigour’, a concept which underscores the importance of transparent documentation of programmatic decision making processes during programme adaptations. In particular this learning brief discusses the use of nested theories of change as a mechanism to enable adaptive rigour within the 'multitude of smalls' approach in the CREID programme.