Human-Centric Decision-Making Models for Social Sciences

The environment of heterogeneous uncertainty is characterized by the presence of variables in multiple uncertainty formalisms. This paper provides an overview of decision models under several uncertainty frameworks including probability theory, Dempster-Shafer belief function theory and possibility theory. It explores the challenges in pulling them together for decision making. We show that the information of sequence of variable resolution, which was often neglected, actually plays a key role in decision making under heterogeneous uncertainty. A novel approach, based on the well-known folding-back principle, to find the certainty equivalent of acts under heterogeneous uncertainty is proposed.

The attempts of this paper are as follows: clarifying the fundamental differences between the one-shot decision theory which was initially proposed in the paper [16] and other decision theories under uncertainty to highlight that the one-shot decision theory is a scenario-based decision theory instead of a lottery-based one; pointing out the instinct problems in other decision theories to show that the one-shot decision theory is necessary to solve one-shot decision problems; manifesting the relation between the one-shot decision theory and the probabilistic decision methods. As regret is a common psychological experience in one-shot decision making, we propose the one-shot decision methods with regret in this paper.

This chapter summarizes and discusses methodologies and findings of recent research focused on the influence of emotion on decision-making in general and charitable giving in particular. Exploring how appraisal theory findings carry over to the decision of charitable giving, we experimentally examine the influence of incidental sadness and anger on charitable donations to an identified or a statistical victim. First, subjects viewed a previously validated film clip and provided a written response to how they would feel in the situation in the clip. Subjects then viewed a charity letter and had the opportunity to make a donation. Overall, participants in both the sad and angry conditions donated more than participants in the control condition. Sad individuals donated more money to a statistical victim relative to individuals in a neutral condition. This finding is consistent with appraisal-tendency theories. Angry individuals, however, did not donate significantly more to either an identified or statistical victim relative to individuals in a neutral condition. Self-reported emotions reveal discrete levels of sadness elicited in the sad condition, but elevated levels of additional negative emotions in the anger conditions.

This chapter presents a relatively new and rapidly developing interdisciplinary theory of decision making, the theory of fast and frugal heuristics. It is first shown how the theory complements most of the standard theories of decision making in the social sciences such as Bayesian expected utility theory and its variants: Fast and frugal heuristics are not derived from normatively compelling axioms but are inspired by the simple rules of thumb that people and animals have been empirically found to use. The theory is illustrated by presenting the basic concepts and mathematics of some fast and frugal heuristics such as the recognition heuristic, the take-the-best heuristic, and fast and frugal trees. Then, applications of fast and frugal heuristics in a number of problems are described (how do laypeople make investment decisions? how do military staff identify unexploded ordnance buried in the ground? how do doctors decide whether to admit a patient to the emergency care or not?) It is emphasized that there are no good or bad decision models per se but that all models can work well in some situations and not in others, and thus the goal is to find the right model for each situation. Accordingly, in all applications, the performance of fast and frugal heuristics is compared, by computer simulations and mathematical analyses, to the performance of standard models such as Bayesian networks, classification-and-regression trees and support-vector machines. Finally, ways of combining standard decision theory and rules of thumb are discussed.

In this chapter, we propose a multi-person decision making procedure where agents judge the alternatives through linguistic expressions generated by an ordered finite scale of linguistic terms (for instance, ‘very good’, ‘good’, ‘acceptable’, ‘bad’, ‘very bad’). If the agents are not confident about their opinions, they might use linguistic expressions composed by several consecutive linguistic terms (for instance, ’between acceptable and good’). The procedure we propose is based on distances and it ranks order the alternatives taking into account the linguistic information provided by the agents. The main features and properties of the proposal are analyzed.

This chapter introduces a version of Cumulative Prospect Theory in a quantile utility model with multiple priors on possible events as proposed in [8]. The chapter analyzes the decision-maker’s risk and ambiguity perception facing ordinary and exterme events. It is showed a new functional that models asymmetric attitude with respect to ambiguity on extreme events (optimism respects windfall gains and pessimism respects catastrophic events) and the decision-maker’s attitude to consider maximization of entropy as a rule of inference. Finally, it is defined a simplified approach based on the epsilon contamination method of a probability distribution.

This chapter proposes a model of covering and discovering processes for solving non-trivial decision making problems in changeable spaces, which encompass most of the decision making problems that a person or a group of people encounter at individual, family, organization and society levels. The proposed framework fully incorporates two important aspects of the real-decision making process that are not fully considered in most of the traditional decision theories: the cognitive aspect and the psychological states of the decision makers and their dynamics. Moreover, the proposed model does not assume that the set of alternatives, criteria, outcomes, preferences, etc. are fixed or depend on some probabilistic and/or fuzzy parameter with known probability distribution and/or membership function. The model allows the creation of new ideas and restructuring of the decision parameters to solve problems. Therefore, it is called decision making/optimization in changeable spaces (DM/OCS). DM/OCS is based on Habitual Domain theory, the decision parameters, the concept of competence set and the mental operators 7-8-9 principles of deep knowledge. Some illustrative examples of challenging problems that cannot be solved by traditional decision making/optimization techniques are formulated as DM/OCS problems and solved. Finally, some directions of research are provided in conclusion.

To make a decision, we must find out the user’s preference, and help the user select an alternative which is the best—according to these preferences. Traditional utility-based decision theory is based on a simplifying assumption that for each two alternatives, a user can always meaningfully decide which of them is preferable. In reality, often, when the alternatives are close, the user is often unable to select one of these alternatives. In this chapter, we show how we can extend the utility-based decision theory to such realistic (interval) cases.

This chapter presents a new approach to dealing with imprecision in the Classical Consumer Utility Theory based on the concept of Marginal Rate of Substitution (MRS) and using the concept of fuzzy sets and fuzzy numbers. The methodology developed applies imprecision to MRS, whereas previous studies placed the imprecision factor on final utility values and functions. The chapter considers fuzzy elements applied to MRS and uses the necessary formulations to obtain the results in Utility Theory. In this fuzzy environment, the final consumer decision problem is framed as a fuzzy nonlinear programming problem, maintaining the classical structure in which consumers maximize their fuzzy utility subject to budget constraints, and showing that the consumer optimum choice is a fuzzy set. The chapter will also address the problem of aggregation of utility functions in order to offer a multi-criteria approach.

Rational decisions are based on information usually uncertain, imprecise and incomplete. The existing decision theories deal with three levels of generalization of decision making relevant information: numerical valuation, interval valuation and fuzzy number valuation. The classical decision theories, such as expected utility theory proposed by von Neumann and Morgenstern, and subjective expected utility theory proposed by Savage use the first level of generalization, i.e. numerical one. These approaches require that the objective probabilities or subjective probabilities and utility values be precisely known. But in real world in many cases it becomes impossible to determine the precise values of needed information. Interval analysis and classical fuzzy set theories have been applied in making decisions and many fruitful results have been achieved. But a problem is that in the mentioned above decision theories the reliability of the decision relevant information is not well taken into consideration. Prof. L. Zadeh introduced the concept of Z-numbers to describe the uncertain information which is more generalized notion closely related with confidence (reliability). Use of Z-information is more adequate and intuitively meaningful for formalizing information structure of a decision making problem. In this chapter we consider two approaches to decision making with Z-information. The first approach is based on reducing of Z-numbers to classical fuzzy numbers, and generalization of expected utility approach and use of Choquet integral with an integrant represented by Z-numbers. A fuzzy measure is calculated on a base of a given Z-information. The second approach is based on direct computation with Z-numbers. To illustrate a validity of suggested approaches to decision making with Z-information the numerical examples are used.

In the process of quantitative decision making, the bounded rationality of real individuals leads to elicitation of interval estimates of probabilities and utilities. This fact is in contrast to some of the axioms of rational choice, hence the decision analysis under bounded rationality is called fuzzy-rational decision analysis. Fuzzy-rationality in probabilities leads to the construction of x-ribbon and p-ribbon distribution functions. This interpretation of uncertainty prohibits the application of expected utility unless ribbon functions were approximated by classical ones. This task is handled using decision criteria Q under strict uncertainty—Wald, maximax, Hurwicz\({}_{\alpha }\), Laplace—which are based on the pessimism-optimism attitude of the decision maker. This chapter discusses the case when the ribbon functions are linearly interpolated on the elicited interval nodes. Then the approximation of those functions using a Q criterion is put into algorithms. It is demonstrated how the approximation is linked to the rationale of each Q criterion, which in three of the cases is linked to the utilities of the prizes. The numerical example demonstrates the ideas of each Q criterion in the approximation of ribbon functions and in calculating the Q-expected utility of the lottery.

The solutions to ill-structured decision problems greatly rely upon the intuition and cognitive abilities of a decision maker because of the vague nature of such problems. To provide decision support for these problems, a decision support system (DSS) must be able to support a user’s cognitive abilities, as well as facilitate seamless communication of knowledge and cognition between itself and the user. This study develops a cognitive decision support system (CDSS) based on human-centric semantic de-biased associations (SDA) model to improve ill-structured decision support. The SDA model improves ill-structured decision support by refining a user’s cognition through reducing or eliminating bias and providing the user with validated domain knowledge. The use of semantics in the SDA model facilitates the natural representation of the user’s cognition, thus making the transfer of knowledge/cognition between the user and system a natural and effortless process. The potential of semantically defined cognition for effective ill-structured decision support is discussed from a human-centric perspective. The effectiveness of the approach is demonstrated with a case study in the domain of sales.

Empirical research shows that humans face many kinds of uncertainties, responding in different ways to the variations in situational knowledge. The standard approach to risk, based largely on rational choice conceptualization, fails to sufficiently take into account the diverse social and psychological contexts of uncertainty and risk. The article addresses this challenge, drawing on sociological game theory (SGT) in describing and analyzing risk and uncertainty and relating the theory’s conceptualization of judgment and choice to a particular procedure of multi-criteria decision-making uncertainty, namely the TOPSIS approach. Part I of the article addresses complex risk decision-making, considering the universal features of an actor’s or decision-maker’s perspective: a model or belief structure, value complex, action repertoire, and judgment complex (with its algorithms for making judgments and choices). Although these features are universal, they are particularized in any given institutional or sociocultural context. This part of the article utilizes SGT to consider decision-making under conditions of risk and uncertainty, taking into account social and psychological contextual factors. Part II of the article takes up an established method, TOPSIS with Belief Structure (BS), for dealing with multi-criteria decision-making under conditions of uncertainty. One aim of this exercise is to identify correspondences between the SGT universal architecture and the operative components of the TOPSIS method. We expose, for instance, the different value components or diverse judgment algorithms in the TOPSIS procedure. One of the benefits of such an exercise is to suggest ways to link different decision methods and procedures in a comparative light. It deepens our empirical base and understanding of values, models, action repertoires, and judgment structures (and their algorithms). The effort here is, of course, a limited one.

The negotiation is a complex decision-making process in which two or more parties talk with one another in afford to resolve their opposing interests. It can be divided into consecutive stages, namely: pre-negotiation phase involving structuring the problem and the analysis of preferences, the intention phase involving the iterative exchange of offers and counter-offers, and the postoptimization phase aiming at the improvement of the agreement obtained in the intention phase. In this chapter, we focus on the analysis of negotiators\({^\prime }\) preferences in ill-structured negotiation problems. We employ the modified FTOPSIS approach and the AHP method for determining the negotiation offers\({^\prime }\) scoring system, which allows for the easy evaluation of both the incoming offers as well as the packages under preparation. The imprecision and vagueness of the packages and option\(\text {s}{^\prime }\) descriptions is modeled by the fuzzy triangular numbers. The Analytic Hierarchy Process is used to derive the negotiation issue weights instead of directly assigning such values to the issues (a classic approach). The FTOPSIS method is used to build the final scoring system allowing for the evaluation of any potential negotiation package. The whole process of negotiation supported by the approach we proposed is illustrated with an numerical example.

Property investment in the real estate industry entails high cost and high risk, but provides high yield for return on investment. Risk factors in the real estate industry are mostly uncertain and change dynamically with the surrounding developments. There are many existing risk analysis tools or techniques that help investors to find better solutions. Most techniques available refer to expert’s opinions in ranking and weighting the risk factors. As a result, they create misinterpretation and varying judgments from the experts. In addition, investment purposes differ between investors for both commercial and residential properties. There is therefore a need for personalisation elements to enable investors to interact with the analysis. This chapter presents a personalised risk analysis model that enables investors to analyse the risk of their property investments and make correct decisions. The model has three main components: investor, decision support technologies, and the data. Real world data from the Australian real estate industry is used to validate the proposed model.

In this paper we present some views on ontologies and assessments, and the relation between logic and guidelines within municipal decision-making in elderly care. Logic is seen, on the one hand, as carrier of information, and, on the other hand, as including mechanisms for inference as underlying decision-making. The ontology and logic for the framework is based on a non-classical typing system where uncertainty is canonically developed in a category theory framework involving term monads both composed with other monads, and as viewed over other categories than just the category of sets. The main question is where uncertainty actually resides, so that they are canonically retrieved rather than amalgamated in ad hoc approaches.

Major decisions are made without advance knowledge of their consequences including decision on energy options. In spite of the best efforts initiated in the development of renewable energy resources, it is too early to visualize that the ever-increasing gap between supply and demand of energy, for peaceful purposes, should be bridged in the near future. A mix of low carbon sources, including nuclear energy and renewable energy, while limiting greenhouse gases is considered a viable solution with less/no computations. In this chapter, a brief write up on Kahneman and Tversky’s Prospect Theory is presented. Authors believe that the perception of gain, loss and risk are intrinsically fuzzy due to limited or no information about the future scenario. Computing with words, a facet of Restriction—Centered Theory of Reasoning and Computation (RCC) proposed by Prof. Lotfi A. Zadeh, could therefore be a useful armamentarium in decision making under risk and uncertainty. The case study, describing decision-making for the energy prospects (options) in India under risk and uncertainty, is presented by using prospect theory, type-1 and type-2 fuzzy relational calculus- a subset of Computing with Words. A commentary on safety of nuclear plants in India is an integral part of the chapter.