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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 reports a new experimental test of the notion that behavior switches from risk averse to risk seeking when gains are “reflected” into the loss domain. We conduct a sequence of experiments that allows us to directly compare choices under reflected gains and losses where real and hypothetical payoffs range from several dollars to over $100. Lotteries with positive payoffs are transformed into lotteries over losses by multiplying all payoffs by –1, that is, by reflecting payoffs around zero. When we use hypothetical payments, more than half of the subjects who are risk averse for gains turn out to be risk seeking for losses. This reflection effect is diminished considerably with cash payoffs, where the modal choice pattern is to exhibit risk aversion for both gains and losses. However, we do observe a significant difference in risk attitudes between losses (where most subjects are approximately risk neutral) and gains (where most subjects are risk averse). Reflection rates are further reduced when payoffs are scaled up by a factor of 15 (for both real and hypothetical payoffs).

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

The focus of this paper is on altruism and coordination among agents with different income levels. A special form of altruism (fairness based or ethical altruism) is investigated by means of experiments. The definition of altruism used here follows from A. Sen’s concept of obligation, i.e. behaviour that produces advantage for someone whose welfare is not important at all for the agent’s well-being. In this sense, the paper investigates altruism without reciprocity. A second hypothesis investigated is that the extent of ethical altruism is influenced by gender and by income differences within the population.

In this chapter, we support that pricing carbon emissions policies in aviation might have a lower than expected impact on aviation activity, if such policies are accompanied by investments in environmental technology and an adequate communication strategy by the airlines, which eliminates information asymmetries. Furthermore, we suggest that firms' environmental strategies might be an important advantage in competition. Using orthogonal design techniques, we developed a binary choice experiment, in which carbon emissions are presented to 338 German respondents among other choice-related attributes. Estimating several binary logit models, we are in a position to compute the air travelers' willingness to pay (WTP) for carbon emissions reductions. Findings suggest that passengers consider carbon emissions in their individual airline (itinerary) choice decision, if they receive the corresponding information. We also show that the passengers' WTP for carbon emissions reductions is in line with recent estimates on marginal damage cost. Furthermore, we find that WTP increases for passengers with high environmental sensitivity, whereas it decreases for frequent flyers. Investments in environmental technology and airline commitment for environmental-related firm policy positively influence the passengers' choices. It appears that the average passenger is more sensitive to environmental issues and is willing to pay more for environmental protection than industry officials or policymakers presume.

While it is widely recognized that firms in an era of technological ferment exist under conditions of significant uncertainty and ambiguity, little is known about the exact processes through which firms explore their ideas and resolve uncertainty. Arguing that our understanding of the era of ferment is much less developed than other aspects of the technology life cycle, we examine the micro-dynamics of technology-based entrepreneurial firms during this period. We focus on the role of purposeful experimentation as a key form of learning for start-ups firms in the era of ferment. Our approach contrasts with the prevailing view in the literature in which the era of ferment is characterized by extensive experimentation across firms, with each firm representing a single data point in an industry-level experiment. It also extends the learning literature by focusing on start-ups and taking the perspective that learning can encompass purposeful experimentation as well as local search and chaotic adaptation in the era of ferment. Building on the literature on experimental design, we propose a definition and taxonomy of purposeful experimentation. The taxonomy defines the experimental landscape as having three domains – technological, product and business model; and two dimensions – degree of simultaneity and degree of parameter manipulation. We examine this framework using data from a technology-based start-up and find evidence for purposeful experimentation as a key element of the firm’s learning strategy. We also highlight the organizational constraints and challenges that are associated with experimentation. Our findings emphasize the importance of entrepreneurial action, choice and internal experimentation processes.

If we are to examine the role of “controls” in different experimental settings, it is appropriate that the word be defined carefully. The Oxford English Dictionary (Second Edition) defines the verb “control” in the following manner: “To exercise restraint or direction upon the free action of; to hold sway over, exercise power or authority over; to dominate, command.” So the word means something more active and interventionist than is suggested by it’s colloquial clinical usage. Control can include such mundane things as ensuring sterile equipment in a chemistry lab, to restrain the free flow of germs and unwanted particles that might contaminate some test.

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).