An experimental study of risk preference in lotteries.

Coombs, Clyde H.; Donnel, Michael L.; Kirk, Daniel

doi:10.1037/0096-1523.4.3.497

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…The greater inconsistency of data generated by pick l/p methods relative to reject I/p methods reported by Coombs et al (1978) was replicated in this study. However, this was only true under certain circumstances.…”

Section: Discussionsupporting

confidence: 68%

“…Once the preference orderings were constructed, the number of inconsistencies were determined. One approach is to divide in half the sum of the absolute differences between the expected frequencies obtained from the Method of Expected Matrices (Coombs, 1964) and the resultant frequencies derived from the data (e.g., Coombs et al, 1978). This method, however, only establishes a lower bound for the number of inconsistencies.…”

Section: Determination Of the Number Of Inconsistenciesmentioning

confidence: 99%

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Response mode and choice consistency: A test of unfolding theory

Carlson

1984

Journal of Mathematical Psychology

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Journal of Experimental Psychology, 4, 497-512), in a study of risk preferences, collected data using both pick 1 and reject $ response modes. Although the preference orders derived from the two response modes were identical, the pick data contained a greater number of inconsistencies than the reject data. In the present study, predictions were derived from unfolding theory (Coombs, 1964. A theory of data. New York: Wiley) regarding the relative consistency of pick and reject response modes. An experiment performed as a test of these predictions supported the unfolding model suggesting that differences in inconsistency between response modes could be attributed to the fineness of the grid of working midpoints imposed upon the choice process by the response mode.

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Section: Discussionsupporting

confidence: 68%

Section: Determination Of the Number Of Inconsistenciesmentioning

confidence: 99%

Response mode and choice consistency: A test of unfolding theory

Carlson

1984

Journal of Mathematical Psychology

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“…The situation parallels several which have appeared in the literature. One example is that of models of choice which incorporate "risk" as a construct, in the absence of a well-characterized concept of risk (e.g., Coombs, Donnell, & Kirk, 1978;Coombs & Huang, 1970;Libby & Fishburn, 1977;Slavic, 1964). The approach is to extract some property, or properties, of the undefined concept about which there is consensus.…”

Section: Differentiating the Modelsmentioning

confidence: 99%

An empirical evaluation of descriptive models of ambiguity reactions in choice situations

Curley

Yates

1989

Journal of Mathematical Psychology

120

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Ambiguity is uncertainty about an option's outcome-generating process, and is characterized as uncertainty about an option's outcome probabilities. Subjects, in choice tasks, typically have avoided ambiguous options. Descriptive models are identified and tested in two studies which had subjects rank monetary lotteries according to preference. In Study 1, lotteries involved receiving a positive amount or nothing, where P denotes the probability of receiving the nonzero amount. Subjects were willing to forego expected winnings to avoid ambiguity near P = SO and P = .75. Near P = .25, a significant percentage of subjects exhibited ambiguity seeking, with subjects, on average, willing to forego expected winnings to have the more ambiguous option. The observed behavior contradicts the viability of a proposed lexicographic model. Study 2 tested four polynomial models using diagnostic properties in the context of conjoint measurement theory. The results supported a sign dependence of ambiguity with respect to the probability level P, such that subjects' preference orderings over ambiguity reversed with changes in P. This behavior was inconsistent with all the three-factor polynomial models investigated. Further analyses failed to support a variant of portfolio theory, as well. The implications of these results for the descriptive modeling of choice under ambiguity are discussed.

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“…When presented with a larger set of alternatives, we are generally expected to exhibit a similar preference ranking regardless of whether we choose the alternatives starting with the best and ending at the worst, or reject them starting at the worst and ending with the best. Coombs, Donnell, and Kirk (1978), for example, document similar rankings of three lotteries obtained by choice and by rejection. The lotteries used by these investigators, however, were unlikely to yield the kind of compatibility effects documented above.…”

Section: Nonbinary Problemsmentioning

confidence: 99%

Choosing versus rejecting: Why some options are both better and worse than others

1993

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A previously unobserved pattern of choice behavior is predicted and corroborated. In line with the principle of compatibility, according to which the weighting of inputs is enhanced by their compatibility with output, the positive and negative dimensions of options (their pros and cons) are expected to loom larger when one is choosing and when one is rejecting, respectively. Subjects are presented with pairs of options, one of which-the enriched option-has more positive as well as more negative dimensions than does the other, impoverished, option. Because positive dimensions are weighted more heavily in choosing than in rejecting, and negative dimensions are weighted more heavily in rejecting than in choosing, the enriched option tends to be chosen and rejected relatively more often than the impoverished option. These findings are extended to nonbinary decision problems, and their implications for the rational theory of choice and for everyday decisions are discussed.Decision theory is concerned with the behavior of people who are faced with the problem of choosing among options. Choices are commonly assumed to reflect underlying preferences, which are expected to satisfy a number of basic conditions. Most importantly, the classical theory of preference assumes that each individual has a well-defined preference order (or a utility function) over any set of options. Each option has some personal value, or utility, for the decision maker, a well-established position in his/her preference ordering: If the person prefers option X over option Y, he/she should not also prefer option Y over option X (von Neumann & Morgenstern, 1947). In addition, if an individual has clear and stable preferences, these should be invariant across normatively equivalent methods of assessing preference and across logically equivalent ways of describing the options.Recent studies of decision making indicate that the foregoing assumptions do not always hold, because people tend not to have well-defined values and preferences. Making decisions is often hard, because we do not know how to trade off one attribute (e.g., salary) against another (e.g., leisure), how to predict the pleasure or pain of future consequences, or, for that matter, what exactly matters to us most (Goldstein, 1990;Kahneman & Snell, 1990;March, 1978;Slovic, Fischhoff, & Lichtenstein, 1982). Hence, we often arrive at a decision problem not with well-established and clearly ranked preferences, but rather with the need to determine our preferences as a result of having to decide. Preferences, it turns out, are

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An experimental study of risk preference in lotteries.

Cited by 12 publications

References 14 publications

Response mode and choice consistency: A test of unfolding theory

Response mode and choice consistency: A test of unfolding theory

An empirical evaluation of descriptive models of ambiguity reactions in choice situations

Choosing versus rejecting: Why some options are both better and worse than others

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