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This paper seeks to use systemic thinking for the purpose of criticizing neoclassical utility theory.

First, the systemic‐theoretical ontology, epistemology and methodology are presented. Then the basis for the utility theory, and later spin‐offs, the decision theory and game theory, for which among others Jon Elster is a main agent, are criticized.

The psychological hypothesis, on which the utility theory is based, is rejected as untrue.

The fact that a theory can be explained in simple mathematical terms may make it popular, but this will hardly make it more scientific, despite its display of numerous mathematical terms. This paper's contribution has been to provide a critique of this concept.

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.

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.

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).

The author presents new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape with fixed point well in the interior of the (0,1) interval: Instead the author usually finds populations dominated by “optimists” who uniformly overweight best outcomes in risky options. The choice pairs used here mostly do not provoke similarity-based simplifications. In a third experiment, the author shows that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.

This paper aims to study the value of sociality. Recent experimental evidence has brought to light that the assumptions of the Prospect Theory by Kahneman and Tversky do not hold in the proposed substantive domain of “sociality”. In particular, the desire to be a part of the social environment, i.e. the environment where individuals make decisions among their peers, is not contingent on the framing. Evolutionary psychologists suggest that humans are “social animals” for adaptive reasons. However, entering a social relationship is inherently risky. Therefore, it is extremely important to know how much people value “sociality”, when the social outcomes are valued more than material outcomes and what kinds of adaptations people use.

We develop a new theory and propose the general utility function that features “sociality” component. We test the theory in the laboratory experiments carried out in several countries.

Our results suggest that when stakes are low the theory of “sociality” is successful in predicting individual decisions: on average, people do value “sociality” and it surpasses the monetary loss.

The main contribution of this paper is the breakdown of the risk attitudes under low stakes and individual level of decision-making. Another advancement is the ability to formalize the social utility or the theory of “sociality” in an economic model; we use general utility function that we define both on the outcomes and on the process of the decision-making itself and test in laboratory studies.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

Neo‐classical utility theory has withstood several decades of sustained criticism. Its success has been due (1) to the ability of the theory to represent an essentially non‐analytical process by analytical methods, and (2) to the fact that the theory was developed for, and applied to, advanced market economies where the simplifying assumptions are most appropriate. It is argued below that the neo‐classical formulation is inappropriate in societies where agrarian traditions predominate and that, consequently, economic policies based on such an approach have frequently been misdirected.

This article aims to develop a new theory that can better explain and predict how and when humans interact with commercial robots. To this end, utility maximization theory (UMT) along with four principles and propositions that may guide how human-to-commercial robot interactions are developed.

This article conceptualizes UMT by drawing from social exchange, conservation of resources, and technology-driven theories.

This article proposes UMT, which consists of four guiding principles and propositions. First, it is proposed that the human must invest sufficient resources to initiate a human-to-commercial robot interaction. Second, the human forms an expectation of utility gain maximization once a human-to-commercial robot interaction is initiated. Third, the human severs a human-to-commercial robot interaction if the human is unable to witness maximum utility gain upon the interaction. Finally, once the human severs a human-to-commercial robot interaction, the human seeks to reinvest sufficient resources in another human-to-commercial robot interaction with the same expectation of utility maximization.

This article is one of the few studies that offers a theoretical foundation for understanding the interactions between humans and commercial robots. Additionally, this article provides several managerial implications for managing effective human-to-commercial robot interactions.