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We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

Much of the literature on theories of decision making under risk has emphasized differences between theories. One enduring theme has been the attempt to develop a distinction between “normative” and “descriptive” theories of choice. Bernoulli (1738) introduced log utility because expected value theory was alleged to have descriptively incorrect predictions for behavior in St. Petersburg games. Much later, Kahneman and Tversky (1979) introduced prospect theory because of the alleged descriptive failure of expected utility (EU) theory (von Neumann & Morgenstern, 1947).

This chapter conducts a systematic comparison of behavioral economics’s challenges to the standard accounts of economic behaviors within three dimensions: under risk, over time, and regarding other people. A new perspective on two underlying methodological issues, i.e., inter-disciplinarity and the positive/normative distinction, is proposed by following the entanglement thesis of Hilary Putnam, Vivian Walsh, and Amartya Sen. This thesis holds that facts, values, and conventions have inter-dependent meanings in science which can be understood by scrutinizing formal and ordinary language uses. The goal is to provide a broad and self-contained picture of how behavioral economics is changing the mainstream of economics.

A research stream known as prospect theory describes how decision biases lead to results that differ from those predicted by classical utility theory (Kahneman and Tversky, 1979). Prospect theory hypothesizes that individuals will experience potential losses more intensely than potential gains, and will be more risk‐seeking in loss situations, while more risk‐avoiding in gain situations. This study includes 948 participants from the PRC and 318 students from the USA. All of our attempts to replicate these findings in the Peoples’ Republic of China have revealed a different pattern. Chinese subjects consistently demonstrated risk‐seeking preferences, both in gain and loss situations.

To: evaluate Prospect Theory and Cumulative Prospect Theory as functional models of decision making and risk within various contexts; compare and analyze risk models and decision‐making models; evaluate models of stock risk developed by Robert Engle and related models; establish whether the models are related and have the same foundations; relate risk, decision making and options theory; and develop the foundations for a new model of decision making and risk named “belief systems”.

Critiques existing academic work in different contexts. Analyzes the shortcomings of various measures of risk, and group decision making, which was not addressed in developing Prospect Theory and Cumulative Prospect Theory. Develops the characteristics of a mew model for decision making and risk named “belief systems”, and then differentiates it from belief networks.

Decision making is a multi‐factor, multi‐dimensional process that often requires the processing of information, and thus, it is inaccurate to impose rigid models in decision making; the existing metrics for quantifying risk are inadequate; Prospect Theory and Cumulative Prospect Theory were developed using questionable methods and data, and are impractical; the analysis of probabilistic insurance and most of the theories and “effects” developed by Kahneman and Tversky's articles are invalid and impractical; Prospect Theory, Cumulative Prospect Theory, Expected Utility Theory, and market‐risk models are conceptually the same and do not account for many facets of risk and decision making; risk and decision making are better quantified and modeled using a mix of situation‐specific dynamic, quantitative and qualitative factors; belief systems can better account for the multi‐dimensional characteristics of risk and decision making.

Areas for further research include: development of dynamic market‐risk models that incorporate asset‐market psychology, liquidity, market size, frequency of trading, knowledge differences among market participants, and trading rules in each market; and further development of concepts in belief systems.

Decision making and risk assessment are multi‐criteria processes that typically require some processing of information, and thus cannot be defined accurately by rigid quantitative models; Prospect Theory and Cumulative Prospect Theory are abstract, rigid, and are not practical models for decision making; and existing market‐risk models are inaccurate, and thus the international financial system may be compromised.

The issues discussed are relevant to government regulators, central banks, judges, risk managers, executives, derivatives regulators, stock exchange regulators, legislators, psychologists, boards of directors, finance professionals, management science/operations research professionals, health‐care‐informatics professionals, scientists, engineers, and people in any situation that requires decision making and risk assessment.

To: evaluate Prospect Theory and Cumulative Prospect Theory as functional models of decision making and risk within various contexts; compare and analyze risk models and decision‐making models; evaluate models of stock risk developed by Robert Engle and related models; establish whether the models are related and have the same foundations; relate risk, decision making and options theory; and develop the foundations for a new model of decision making and risk named “belief systems”.

Critiques existing academic work in different contexts. Analyzes the shortcomings of various measures of risk, and group decision making, which was not addressed in developing Prospect Theory and Cumulative Prospect Theory. Develops the characteristics of a mew model for decision making and risk named “belief systems”, and then differentiates it from belief networks.

Decision making is a multi‐factor, multi‐dimensional process that often requires the processing of information, and thus, it is inaccurate to impose rigid models in decision making; the existing metrics for quantifying risk are inadequate; Prospect Theory and Cumulative Prospect Theory were developed using questionable methods and data, and are impractical; the analysis of probabilistic insurance and most of the theories and “effects” developed by Kahneman and Tversky's articles are invalid and impractical; Prospect Theory, Cumulative Prospect Theory, Expected Utility Theory, and market‐risk models are conceptually the same and do not account for many facets of risk and decision making; risk and decision making are better quantified and modeled using a mix of situation‐specific dynamic, quantitative and qualitative factors; belief systems can better account for the multi‐dimensional characteristics of risk and decision making.

Areas for further research include: development of dynamic market‐risk models that incorporate asset‐market psychology, liquidity, market size, frequency of trading, knowledge differences among market participants, and trading rules in each market; and further development of concepts in belief systems.

Decision making and risk assessment are multi‐criteria processes that typically require some processing of information, and thus cannot be defined accurately by rigid quantitative models; Prospect Theory and Cumulative Prospect Theory are abstract, rigid, and are not practical models for decision making; and existing market‐risk models are inaccurate, and thus the international financial system may be compromised.

The issues discussed are relevant to government regulators, central banks, judges, risk managers, executives, derivatives regulators, stock exchange regulators, legislators, psychologists, boards of directors, finance professionals, management science/operations research professionals, health‐care‐informatics professionals, scientists, engineers, and people in any situation that requires decision making and risk assessment.

This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization.

The authors also cover related disciplines such as trading rules, contagion and various econometric aspects.

While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint.

The research paper is original.

The recent COVID-19 virus outbreak, as well as many other global risks, has put the tourism industry on the brink of collapse. Even if interesting advances have been seen the light of publicity based on risk perception, no less true seems to be that the current theory is not enough to understand and describe the impacts of new global risks that may destroy the industry in question of weeks. This introductory chapter gives a snapshot on the fragile conditions we are moving today and interrogates further the future of tourism.

Since it was adopted from psychology just after the turn of twentieth century, risk perception theory has multiplied and successfully evolved in the fields of tourism research. A dearth of studies has focused on risk perception over the recent decades. We have certainly identified three clear-cut traditions: demographical school, psychological school and critical perspective. With benefits and problems each theory has shed light trying to measure the impact of global risks in the tourism and hospitality industries.

This book keeps the originality to update what has been published in the earlier decades. We are incorporating new topics to the discussion as well as new epistemologies and methodologies. Gathering different high-quality products authored by well-renowned authors coming from different countries, it lays the foundations to a new understanding of risk perception and tourism safety-security debating the next steps and the real challenges posed on the industry in a not so long near future.

This study aims to develop a comprehensive adoption model that combines constructs from various theories and tests these theories against each other. The study combines a technology acceptance model, innovation diffusion theory and risk theory. It develops this model in a smart home applications context.

The study is based on an online survey consisting of 409 participants, and the data are analyzed using structural equation modeling.

Each theory provides unique insights into technology acceptance and numerous constructs are interrelated. Predictors from innovation diffusion and risk theory often display indirect effects through technology acceptance variables. The study identifies risk perception as a major inhibitor of use intention, mediated through perceived usefulness. Results reveal that the most important determinants of use intention are compatibility and usefulness of the application.

Studies which do not examine different theories together may not be able to detect the indirect effects of some predictors and could falsely conclude that these predictors do no matter. The findings emphasize the crucial role of compatibility, perceived usefulness and various risk facets associated with smart homes.

This study broadens the understanding about the necessity of combining acceptance and adoption drivers from several theories to better understand the usage of complex technological systems such as smart home applications.