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losses from prior endowment mechanism
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losses from prior endowment mechanism: participants received $1.50 prior to participation but could lose more. Participants in real play did play several of the gambles so there is an income effect. %} Slovic, Paul (1969) “Differential Effects of Real versus Hypothetical Payoffs on Choices among Gambles,” Journal of Experimental Psychology 80, 434–437. {% Seems to argue against risk-aversion as a generalized characteristic of individuals, invariant over different settings. %} Slovic, Paul (1972) “Information Processing, Situation Specificity, and the Generality of Risk-Taking Behavior,” Journal of Personality and Social Psychology 22, 128–134. {% Seems to argue against risk-aversion as a generalized characteristic of individuals, invariant over different settings. %} Slovic, Paul (1972) “Psychological Study of Human Judgment: Implications for Investment Decision Making,” Journal of Finance 27, 779–799. {% Shows choice-matching discrepancy. Introduces prominence effect. Argues that probability is the prominent attribute in lotteries with one nonzero outcome. %} Slovic, Paul (1975) “Choice between Equally Valued Alternatives,” Journal of Experimental Psychology: Human Perception and Performance 1, 280–287. {% Easy, accessible review of preference reversals and constructive viewpoint; cites Maclean (unpublished) for medical decision making who argues that preference measurement should be more involved and interactive than the normal approach. P. 369 writes, on the prescriptive purpose of preference construction: “truth ultimately resides in the process, rather than in the outcome.” %} Slovic, Paul (1995) “The Construction of Preference,” American Psychologist 50, 364–371. {% %} Slovic, Paul (2010) “The Feeling of Risk: New Perspectives on Risk Perception.” Earthscan, London. {% Risk averse for gains, risk seeking for losses. Find that stating a problem with risky losses as an insurance question, changes risk seeking attitudes into risk aversion attitudes. %} Slovic, Paul, Baruch Fischhoff, & Sarah Lichtenstein (1982) “Response Mode, Framing, and Information-Processing Effects in Risk Assessment.” In Robin M. Hogarth (ed.) New Directions for Methodology of Social and Behavioral Science: Question Framing and Response Consistency no. 11, 21–36, Josssey-Bass, San Francisco. {% SG higher than CE: Study 5 shows that probability equivalent method gives higher utility than certainty equivalent method Pp. 22-23 suggest that probability is a “prominent dimension” in choices between one-nonzero-outcome-gambles: “In terms of the prominence factor, the more important dimension (i.e., probability) is expected to loom larger in choice than in either matching procedure... both compatibility and prominence are present in the data.” This is contrary to Tversky, Sattath, & Slovic (1988), p. 382. %} Slovic, Paul, Dale Griffin, & Amos Tversky (1990) “Compatibility Effects in Judgment and Choice.” In Robin M. Hogarth (ed.) Insights in Decision Making, A Tribute to Hillel J. Einhorn, 5–27, The University of Chicago Press, Chicago. {% %} Slovic, Paul & Sarah Lichtenstein (1968) “Importance of Variance Preferences in Gambling Decisions,” Journal of Experimental Psychology 78, 646–654. {% P. 3 2nd para: SEU = SEU This paper gave inspiration for the later discovery of preference reversals; nonlinearity in probabilities. %} Slovic, Paul & Sarah Lichtenstein (1968) “Relative Importance of Probabilities and Payoffs in Risk Taking,” Journal of Experimental Psychology Monograph 78 (no.3, Pt. 2) 1–18. {% %} Slovic, Paul & Sarah Lichtenstein (1983) “Preference Reversal: A Broader Perspective,” American Economic Review 73, 596–605. {% Seem to say that small probabilities can be ignored. %} Slovic, Paul, Sarah Lichtenstein, Bernard Corrigan, & Barbara Combs (1977) “Preference for Insuring against Probable Small Losses: Insurance Implications,” Journal of Risk and Insurance 44, 237–258. {% survey on nonEU %} Slovic, Paul, Sarah Lichtenstein, & Baruch Fischhoff (1988) “Decision Making.” In Richard C. Atkinson, Richard J. Herrnstein, Gardner E. Lindzey, & R. Duncan Luce (eds.) Stevens Handbook of Experimental Psychology 2, 673–738, Wiley, New York. {% No real incentives, only hypothetical. In Allais paradox (also Ellsberg paradox), the authors present participants with arguments for/against Savage/Allais. Some more are convinced by Allais’ arguments than by Savage’s. The authors conclude that Savage’s sure-thing principle is not as generally convincing to people as has been thought before. The authors never state explicitly what their own personal opinion is on the normative status of the axiom. This paper reacts to a similar study by MacCrimmon (1968) that did find most participants convinced by Savage’s axioms. Curley, Yates, & Abrams (1986) also gave subjects arguments for and against ambiguity aversion, after which 80% wanted to be ambiguity averse. %} Slovic, Paul & Amos Tversky (1974) “Who Accepts Savage’s Axiom?,” Behavioral Science 19, 368–373. {% Stigler (1950) is enthusiastic about this paper. utility = representational?: p. 1 (where “it” refers to economics) “we must make it completely independent of psychological assumptions and philosophical hypothesis.” According to Stigler, §V, just above A., with Slutsky’s development, introspection no longer plays a significant role in utility theory. He obviously makes this claim for economics. %} Slutsky, Evgeny E. (1915) “Sulla Teoria del Bilancio del Consumatore,” Giornale degli Economisti series 3, 51, 1–26. Translated into English by Olga Ragusa (1952) as: “On the Theory of the Budget of the Consumer.” In George J. Stigler & Kenneth E. Boulding (eds.) Readings in Price Theory, 27–56, Americal Economic Association; RD Irwin Inc, Chicago. {% As nice as its title says. Expectation is projection on constant functions, so special case of conditional expectation, etc. %} Small, Christopher G. & Don L. McLeish (1994) “Hilbert Space Methods in Probability and Statistical Inference.” Wiley, New York. {% People give more donations to dramatic castrophes such as earth quake than to bigger catastrophes such as malaria because they, because of reference point effects, perceive the former as bigger than the latter. %} Small, Deborah A. (2010) “Reference-Dependent Sympathy,” Organizational Behavior and Human Decision Processes 112, 151–160. {% Seems to be the first publication explaining Smet’s pignistic transformation and giving its justification. %} Smets, Philippe (1989) “Constructing the Pignistic Probability Function in a Context of Uncertainty.” In Max Henrion, Ross D. Shachter, Laveen N. Kanal, & John F. Lemmer (eds.) “Uncertainty in Artificial Intelligence 5,” 29–40, North-Holland, Amsterdam. {% %} Smets, Philippe (1991) “The Transferable Belief Model and Other Interpretations of Dempster-Shafer’s Model.” In Piero P. Bonissone, Max Henrion, Laveen N. Kanal, & John F. Lemmer (eds.) Uncertainty in Artificial Intelligence 6, 375–383, Elsevier, Amsterdam. {% updating; Dutch book; Seems to propose a model of dynamic choice within Smets transferable belief model that avoids sure losses and Dutch books. %} Smets, Philippe (1993) “No Dutch Book Can Be Built against the TBM even though Update Is not Obtained by Bayes Rule of Conditioning.” In Romano Scozzafava (ed.) Workshop on Probabilistic Expert Systems, 181–204, Società Italiana di Statistica, Rome, Italy. {% updating; Seems to explain the transferable belief model and to re-explain the pignistic transformation. %} Smets, Philippe & Robert Kennes (1994) “The Transferable Belief Model,” Artificial Intelligence 66, 191–234. {% risky utility u = transform of strength of preference v; 253 farmers were interviewed, face to face, during two consecutive years for risky utility of the price for poratoes, ranging from 10 cents/kg to 70 cents/kg (by CE (certainty equivalent) method) and by direct strength of preference. Thus the total gains or losses depend on how many kgs of potatoes the farmer had, and what the expenses were. For both u and v, exp. functions fitted better than powers. u is exponential transformation of v, remarkably u is less concave than v. remarkably u is less concave than v. CE bias towards EV: that can support the hypothesis that CE questions contain not only biases that enhance risk aversion but also biases in themselves to enhance risk seeking. P. 362 does cross-checks, finds deviations of approximately 8% in cross-checks questions, and concludes from that that internal consistency is good. P. 362: majority exhibited concave utility, it was plausible that they took outcomes as gains. Note that there was one-parameter fitting. %} Smidts, Ale (1997) “The Relationship between Risk Attitude and Strength of Preference: A Test of Intrinsic Risk Attitude,” Management Science 43, 357–370. {% free-will/determinism: free will illusionism: although we have no free will, it is important that we keep the illusion of it. %} Smilansky, Saul (2002) “Free Will and Illusion.” Oxford University Press, Oxford. {% P. 44 seems to argue for diminishing marginal utility %} Smith, Adam (1759-1790) “The Theory of Moral Sentiments” [1976 edn. by David D. Raphael & Alec L. Macfie], Clarendon Press, Oxford. {% utility = representational?: they argue/show that preferences can be predicted from neurodata. P. 2 writes: “Furthermore, since there may also be stable relationships between real choices and a much broader class of nonchoice variables, there is no a priori reason to limit a prediction exercise to elicited preferences.” They use the nice term nonchoice variables. This general point was also central in Abdellaoui, Barrios, & Wakker (2007). %} Smith, Alec, B. Douglas Bernheim, Colin F. Camerer, & Antonio Rangel (2014) “Neural Activity Reveals Preferences without Choices,” American Economic Journal: Microeconomics 6, 1–36. {% Has the concept of utility, between Bernoulli (1738) and Bentham (1789). Put forward the famous water-diamond paradox; i.e., the paradoxical difference between value in use and value in exchange. Water exceeds diamond as regards the former but not the latter. equate risk aversion with concave utility under nonEU: Smith does not do this but clearly distinguishes.: book I, Ch. X, §1 on risky choices between “lotteries” is interesting. Bréban & Lapidus (2012) nicely argue that Smith assumes diminishing marginal utility (1759-1790) “The Theory of Moral Sentiments” [1976 edn., p. 44 seems to be clear on it) but risk seeking, which is a good motivation for RDU. Smith clearly ascribes the risk seeking to overestimation of chance of good fortune. “The chance of gains is by every man more or less overvalued, and the chance of loss is by most men undervalued.” inverse-S (although it does not specify small probability as relevant to inverse-S) P. 210 seems to write: “That the chance of gain is naturally over-valued we may learn from the universal success of lotteries … The vain hope of gaining some of the great prizes is the sole cause of this demand. The soberest people scarce look upon it as a folly to pay a small sum for the chance of gaining ten or twenty thousand pounds.” Smith nicely distinguishes probability overestimation from overestimating own abilities, and seems to write: “The over-weening conceit which the greater part of men have of their own abilities, is an antient evil remarked by the philosophers and moralists of all ages. Their absurd presumption in their own good fortune, has been less taken notice of. It is, however, if possible, still more universal. There is no man living who, when in tolerable health and spirits, has not some share of it. The chance of gain is by every man more or less over-valued, and the chance of loss is by most men under-valued, and by scarce any man, who is in tolerable health and spirits, valued more than it is worth.” (pp. 124-5) On other-regarding preferences, seems to write: “How selfish soever man may be supposed, there are evidently some principles in his nature, which interest him in the fortune of others, and render their happiness necessary to him, though he derives nothing from it except the pleasure of seeing it.” %} Smith, Adam (1776) “An Inquiry into the Nature and Causes of the Wealth of Nations” [1976 edn. by Roy H. Campbell & Andrew S. Skinner], Clarendon Press, Oxford. {% probability intervals; Dutch book; §13, p. 13, introduced lottery-prizes as quantitative outcomes alternative to money, to avoid utility curvature. The story is funny: a very small diamond is put in beeswax, you get y gram, it will be melted, and you find or do not find the diamond. %} Smith, Cedric A.B. (1961) “Consistency in Statistical Inference and Decision,” Journal of the Royal Statistical Society B 23, 1–25. {% Seems to have probability intervals. %} Smith, Cedric A.B. (1965) “Personal Probability and Statistical Analysis,” Journal of the Royal Statistical Society A 128, 469–499. {% %} Smith, Edward E. & Douglas L. Medin (1981) “Categories and Concepts.” Harvard University Press, Cambridge, MA. {% dynamic consistency: examines errors induced by failing to account for possibilities to borrow and lend in risk analyses of cash flows. It is a nice case where the timing of the resolution of uncertainty can rationally matter because of intermediate decisions. %} Smith, James E. (1998) “Evaluating Income Streams: A Decision Analysis Approach,” Management Science 44, 1690–1708. {% Assume the usual QALY model, but add that in addition to health quality per se, there are other things, being consumption of commodities. The model is L-QALY = SUMjqju(cj), where qj is quality of life index, u(cj) utility of consumption of commodity bundle cj, and the person can enjoy the latter only partially, part qj, if in impaired health state. L-QALY designates life-QALY as opposed to health QALY. Analyze some optimization problems under this model. Figure 14 has decision tree for aneurysm. Maybe: simple decision analysis cases using EU %} Smith, James E. & Ralph L. Keeney (2005) “Your Money or Your Life: A Prescriptive Model for Health, Safety, and Consumption Decisions,” Management Science 51, 1309–1325. {% Conservation of influence: flexibility is future influence. They use a consultancy with an oil/gas company to compare standard option pricing techniques (where often a discount rate higher than the risk-free market discount rate is used to reflect extra risks borne) and decision analysis techniques, and show how to integrate them. P. 15 endnote 6: discusses as-if risk-neutral evaluation by market in presence of risk aversion. The paper illustrates several points for applied decision analysis: (1) The major issue in practice is to get the right model. (2a) One should pay attention to future decision options (“flexibility;” (P. 1 1st column l. -4 and throughout). (2b) The finance techniques of pricing the future choice flexibility of options can be useful to evaluate future decisions. (3) One has to trade off completeness of a model and tractability. (P. 3 2nd column 2nd para, that Figure 2 is much too large. 3rd para about 52,500 end points in simplified tree. P. 4 2nd para, discussing for instance getting amount of computer programming). (4) When to use market expectation and when own subjective (p. 9 2nd para penultimate para). P. 9 l. -3: option valuation for market risks and DA for private risks. (5) Iso lognormal distributions assumed in finance, here mean-reverting distributions were better (p. 6 2nd para). This reduces the impact of incorporating flexibility (p. 7 1st column l. -3). %} Smith, James E. & Kevin F. McCardle (1999) “Options in the Real World: Lessons Learned in Evaluating Oil and Gas Investments,” Operations Research 47, 1–15. {% %} James E. Smith & Canan Ulu (2017) “Risk Aversion, Information Acquisition, and Technology Adoption,” Operations Research, forthcoming. {% P. 570: 60% of decision analysis applications is in health. %} Smith, James E. & Detlof von Winterfeldt (2004) “Decision Analysis in Management Science,” Management Science 50, 561–574. {% When choosing a best option, its expected utility is usually overestimated (the optimizer’s curse), so that usually some disappointment will follow. %} Smith, James E. & Robert F. Winkler (2006) “The Optimizer's Curse: Skepticism and Postdecision Surprise in Decision Analysis,” Management Science 52, 311–322. {% Made brain scans of participants (N=9, all medical students) while doing Ellsberg paradox etc. These participants had electrodes in themselves and got radio-active liquids injected every two minutes ... Risk averse for gains, risk seeking for losses: this they find, the participants are risk averse for gains and risk seeking for losses when probabilities are known. Figure 2 shows more risk aversion for gains than risk seeking for losses. ambiguity seeking for losses: they do find less ambiguity avoidance for losses than for gains, but participants are still ambiguity averse also for losses. The reason may be, first, the contrast effect, the choice is directly between known and unknown probability. There is a second reason: participants cannot choose their color in the unknown urn so they may be suspicious (suspicion under ambiguity). This also occurred in Lan, Cherng-Horng, Peter Ayton, & Nigel Harvey (2010). reflection at individual level for ambiguity %} Smith, Kip, John W. Dickhaut, Kevin McCabe, & José V. Pardo (2002) “Neuronal Substrates for Choice under Ambiguity, Risk Certainty, Gains and Losses,” Management Science 48, 711–718. {% To what extent desires (motivated or not, normative or not) are causes of acts. %} Smith, Michael (1987) “The Humean Theory of Motivation,” Mind 96, 36–61. {% %} Smith, Richard D. (1996) “Is Regret Theory an Alternative Basis for Estimating the Value of Health care Interventions?,” Health Policy 37, 105–115. {% P. 325: considers EU to be normative. P. 325 writes: But I do not care for the probabilistic interpretation of the violations. To me probabilities are probabilities in the sense of nonnegativity, additivity and the property of the unit measure over the whole event space. I grant the right of a man to have systematic and deliberate preferences for rewards based on dice game contingencies over the same rewards based on Dow-Jones stock price contingencies. But if he insists also that he is less than certain that the Dow-Jones average will either rise or not rise by five points or more tomorrow, then so far as I am concerned he is now making a “mistake.” He does not understand what is (or should be) meant by probability. He is entitled to his tastes, but not to any new definitions of probability.” P. 325, on Ellsberg-like situations: “…, there may be real or imagined elements of skill which increase or reduce the subjective value of the outcomes “lose” or “win.” ” So he thinks that in, say, Ellsberg two-color paradox, the utility of an outcome can be lower if it results from a color from the unknown urn than from the known urn. I find this a very very weird idea. In the same way as Smith writes on p. 325 l. 6: “probabilities are probabilities” I will say “a dollar is a dollar” where “is” is in the sense of giving the same utility. You can do the same with a dollar if you have it after a black ball from a known urn as after a black ball from an unknown urn. Then he brings in, on p. 325, the competence effect, with social effects of being blamed brought in. second-order probabilities to model ambiguity: p. 329 closing para, suggests that ambiguity is the same as 2nd order probability. %} Smith, Vernon L. (1969) “Measuring Nonmonetary Utilities in Uncertain Choices: The Ellsberg Urn,” Quarterly Journal of Economics 83, 324–329. {% real incentives/hypothetical choice Kachelmeier & Shehata say: the “dominance postulate” has induced incentives in the economics literature (clarified in Smith & Walker, 1993). %} Smith, Vernon L. (1976) “Experimental Economics: Induced Value Theory,” American Economic Review, Papers and Proceedings 66, 274–279. {% Seems to be cited frequently. Formulates conditions for microeconomic experiment: saliency: rewards should be linked to actions of participants; Payoff dominance: reward structure dominates (subjective) costs of participation (e.g., calculation costs). %} Smith, Vernon L. (1982) “Microeconomic Systems as an Experimental Science,” American Economic Review 72, 923–955. {% P. 159, footnote 8, argues for a behavioral preference assumption (constant relative risk aversion) that market data are not well suited to refute it because they are too complex.: I have been asked: “How do you react to criticisms which say that from market data we can reject the assumption of constant relative risk aversion? We can look at how individuals change their portfolio with wealth, and it does not conform even to a much looser specification of the utility function? Why test a theory which has been rejected by market data?” Here are my reactions. (1) We can’t reject the theory from this kind of market data. The data tells us how portfolios change with some measure of “wealth,” confounded with changes in time, income, expectations, information, unmeasured probability assessments, and so on ad infinitum. We can’t learn what we want to know from this sort of exercise independently of some rigorous tests, although market evidence and experimental evidence can illuminate each other. (2) … (3) [(2) and (3) describe two empirical findings that do support constant relative risk aversion] (4) Constant relative risk aversion need not be valid over the entire interval of positive income to yield predictive accuracy over the relevant range of observations. Probably no functional form will be satisfactory everywhere. P. 164 argues that the vNM axioms do not speak to what the outcomes are, apparently taking EU as branch of abstract mathematics rather than as an empirical science: “The axioms of the theory do not tell us what the prizes are.” %} Smith, Vernon L. (1989) “Theory, Experiments and Economics,” Journal of Economic Perspectives 3, 151–169. {% real incentives/hypothetical choice; advances the experimental-economics arguments. Is sometimes highly critical of psychologists, in particular Kahneman & Tversky. For instance, footnote 5 cites a referee saying: “It seems to me that the psychologists have not done their homework.” %} Smith, Vernon L. (1991) “Rational Choice: The Contrast between Economics and Psychology,” Journal of Political Economy 99, 878–897. {% Discusses, a.o., the Duhem-Quine problem: result of experiments can always have been distorted because of confounds due to other assumptions presupposed. %} Smith, Vernon L. (2002) “Method in Experiment: Rhetoric and Reality,” Experimental Economics 5, 91–110. {% %} Smith, Vernon L. (2008) “Rationality in Economics: Constructivist and Ecological Forms.” Cambridge University Press, Cambridge. {% real incentives/hypothetical choice: paying participants reduces variance %} Smith, Vernon L. & James M. Walker (1993) “Monetary Rewards and Decision Cost in Experimental Economics,” Economic Inquiry 31, 245–261. {% %} Smith, Vernon L. & Bart J. Wilson (2017) “Sentiments, Conduct, and Trust in the Laboratory,”.Social Philosophy & Policy 34, 25–55. {% Conflicting evidence is if two experts give different probability estimates. I want to add that special attention should be given to a case where one expert estimates an extreme probability 0 or 1. Say one expert says p=1 and the other p=0.8. Then it is natural that subjects give more weight to the sure expert, and taking the probability-midpoint 0.9 as representative of this state of info is not reasonable. Provided subjects with hypothetical info in the form of interval estimates, and asked them to judge introspectively what constituted conflicting evidence, what ambiguity, what uncertainty, and so on. %} Smithson, Michael J. (1999) “Conflict Aversion: Preference for Ambiguity vs Conflict in Sources and Evidence,” Organizational Behavior and Human Decision Processes 79, 179–198. Smithson, Michael J. (2013) “Conflict and Ambiguity: Preliminary Models and Empirical Tests,” working paper, The Australian National University, C {% %} Smorodinsky, Rann (2000) “The Reflection Effect for Constant Risk Averse Agents,” Mathematical Social Sciences 40, 265–276. {% measure of similarity %} Sneath, Peter H.A. & Robert R. Sokal (1973) “Numerical Taxonomy: The Principles and Practice of Numerical Classification.” Freeman, San Francisco. {% %} Sneddon, Robert (2001) “Bias in a PEST Procedure,” {% Seems that they measured probability weighting, and found that two-parameter family fits best. %} Sneddon, Robert & Robert Duncan Luce (2001) “Empirical Comparisons of Bilinear and Non-Bilinear Utility Theories,” Organizational Behavior and Human Decision Processes 84, 71–94. {% %} Sneed, John D. (1971) “The Logical Structure of Mathematical Physics.” Reidel, Dordrecht. {% %} Sniedovich, Moshe (1986) “C-Programming and the Minimization of Pseudolinear and Additive Concave Functions,” Operations Research Letters 5, 185–189. {% intuitive versus analytical decisions; computer program outperforms professional purchasing managers in predicting likelihood of purchasing transactions. %} Snijders, Chris, Frits Tazelaar, & Ronald Batenburg (2003) “Electronic Decision Support for Procurement Management: Evidence on whether Computers can Make Better Procurement Decisions,” Journal of Purchasing & Supply Management 9, 191–198. {% anonymity protection %} Snijkers, Gert J.M.E. (1988) “Privacy Protection of Statistical Data: Suppressing Cells in Two-Dimensional Tables,” Netherlands Official Statistics 3, 46–47. {% Some theorems where ambiguity averse people will like reduction of ambiguity and the info that generates it, but ambiguity seeking people may not like info that reduces ambiguity. Uses KMM model. P. 134 considers only complete info when discussing info for risk. The claims presented in this paper only consider particular forms of info. For example, for each violation of EU there are situations of ambiguity aversion, but those are not considered in this paper (cf. footnote 5). P. 136 2nd para: note that p. 1863 of KMM only writes that their measure is subjective and not objective, and not in general. The concluding sentence argues that for banking policies such as the recent appointment of Ben Bernanke, the direct effect on welfare is determined by the value of changing ambiguity and that we can infer this from the mathematical formulas of this paper. %} Snow, Arthur (2010) “Ambiguity and the Value of Information,” Journal of Risk and Uncertainty 40, 133–145. {% The author uses recursive expected utility. P. 30 argues that Choquet expected utility cannot separate ambiguity from ambiguity attitude, but this is not so. There are similar discussions of related models. %} Snow, Arthur (2011) “Ambiguity Aversion and the Propensities for Self-Insurance and Self-Protection,” Journal of Risk and Uncertainty 42, 27–43. {% Seems to find violation of RCLA %} Snowball, Dough & Clif Brown (1979) “Decision Making Involving Sequential Events: Some Effects of Disaggregated Data and Dispositions toward Risk,” Decision Sciences 10, 527–546. {% Use data on bets on US horse races between 1992 and 2001 to test whether utility curvature alone, or probability weighting alone, better fits the data, and find that it is the latter. More precisely, for merely the data from win bets, both models can fit data equivalently, but for predictions in wider sets probability weighting does better, confirming prospect theory. %} Snowberg, Erik, & Justin Wolfers (2010) “Explaining the Favorite-Long Shot Bias: Is It Risk-Love or Misperceptions?,” Journal of Political Economy 118, 723–746. {% foundations of probability; foundations of quantum mechanics; %} Snyder, Douglas M. (1993) “Quantum Mechanics is Probabilistic in Nature,” Journal of Mind and Behavior 14, 145–154. {% Gekregen van Hans Peters. A la Existence of utility functions for the Nash bargaining problem %} Sobel, Joel (1981) “Distortion of Utilities and the Bargaining Problem,” Econometrica 49, 597–619. {% Survey with discussion of altruism, group selection, etc. %} Sobel, Joel (2005) “Interdependent Preferences and Reciprocity,” Journal of Economic Literature 43, 392–436. {% Section I argues that neuroeconomics isn’t yet at the level of maturity and standards of other fields, but this may come. Section II discusses normative economics, and expresses opinions that I fully agree with, being that economics should be open to inputs other than revealed preference, but these inputs should prove their relevance to preference. Also that sometimes there is consensus favoring paternalism, e.g. for young/incompetent decision makers. %} Sobel, Joel (2009) “Comments on Neuroeconomics,” American Economic Journal: Microeconomics 1, 60–67. {% Considers Newcomb’s problem %} Sobel, Jordan H. (1988) “Metatickles, Ratificationism, and Newcomb-like Problems without Dominance.” In Bertrand R. Munier (ed.) Risk, Decision and Rationality, 483–501, Reidel, Dordrecht. {% Considers Newcomb’s problem %} Sobel, Jordan H. (1988) “Defenses and Conservative Revisions of Evidential Decision Theories: Metatickles and Ratificationism,” Synthese 75, 107–131. {% %} Sobel, Jordan H. (1994) “Two Envelopes,” Theory and Decision 36, 69–96. {% %} Sobel, Jordan H. (2004) “On Wakker’s Critique of Allais Preferences,” Croatian Journal of Philosophy 4, 253–272. {% foundations of statistics %} Sohlberg, Staffan & Gerhard Andersson (2005) “Extracting Maximum of Useful Information from Statistical Research Data,” Scandinavian Journal of Psychology 46, 69–77. {% Parody on nonsensiceal bluffing texts. %} Sokal, Alan D. (1996) “Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity,” Social Text 46/47, 217–252. {% Nice that the author knows Theorems 7.1&7.2.2 in Luce & Narens (1985), showing that RDU is the most general interval scale for two states of nature. Many further results are given, using the n-point homogeneity and n-point uniqueness of Luce & Narens. %} Sokolov, Mikhail V. (2011) “Interval Scalability of Rank-Dependent Utility,” Theory and Decision 70, 255–282. {% %} Sokol-Hessner, Peter, Ming Hsu, Nina G. Curley, Mauricio R. Delgado, Colin F. Camerer, & Elizabeth A. Phelps (2009) “Thinking Like a Trader Selectively Reduces Individuals’ Loss Aversion,” Proceedings of the National Academy of Sciences 106, 5035–5040. {% crowding-out: seems he cannot believe what Titmuss claimed on payment for blood. %} Solow, Robert M. (1971) “Blood and Thunder,” Yale Law Journal 80, 170–183. {% %} Soman, Dilip, George Ainslie, Shane Frederick, Xiuping Li, John Lynch, Page Moreau, Andrew Mitchell, Daniel Read, Alan Sawyer, Yaacov Trope, Klaus Wertenbroch, & Gal Zauberman (2005) “The Psychology of Intertemporal Discounting: Why are Distant Events Valued Differently from Proximal Ones?,”Marketing Letters 16, 347–360. {% This paper derives analytical results for regret theory, and tests them empirically. The authors decompose the risk premium (taken in the feedback situation) into two premiums: (1) the resolution premium, which is how much the decision maker would pay for uncertainty not to be resolved (information aversion). The rest is the regret premium, which is what he pays extra relative to an expected utility maximizer. In the absence of transitivity, such concepts are tricky to interpret. The experiment confirms earlier findings on regret aversion, but other findings are less clear. %} Somasundaram, Jeeva & Enrico Diecidue (2017) “Regret Theory and Risk Attitudes,” Journal of Risk and Uncertainty 55, 147–175. {% %} Sommer, Richard & Patrick Supps (1997) “Dispensing with the Continuum,” Journal of Mathematical Psychology 41, 3–10. {% Study the Fatou property for Choquet integrals. %} Song, Yongsheng & Jia-An Yan (2009) “Risk Measures with Comonotonic Subadditivity or Convexity and Respecting Stochastic Orders,” Insurance: Mathematics and Economics 45, 459–465. {% %} Sonnemans, Joep (2006) “Price Clustering and Natural Resistance Points in the Dutch Stock Market: A Natural Experiment,” European Economic Review 50, 1937–1950. {% %} Sonnemans, Joep & Theo Offerman (2001) “Is the Quadratic Scoring Rule Really Incentive Compatible?,” CREED, Dept. of Economics, University of Amsterdam, the Netherlands. {% %} Sonnenberg, Frank A. & Stephen G. Pauker (1987) “Decision Maker: An Advanced Personal Computer Tool for Clinical Decision Analysis.” Proceedings of the Eleventh Annual Symposium on Computer Applications in Medical Care, Washington D.C., IEEE Computer Society. {% %} Sonnenschein, Hugo F. (1965) “The Relationship between Transitive Preference and the Structure of the Choice Space,” Econometrica 33, 624–634. {% %} Sonnenschein, Hugo F. (1971) “Demand Theory without Transitive Preferences, with Applications to the Theory of Competitive Equilibrium.” In John S. Chipman, Leonid Hurwicz, Marcel K. Richter, & Hugo F. Sonnenschein (eds.) “Preferences, Utility, and Demand,” 215–223, Hartcourt, New York. {% %} Sono, Masazo (1945) “The Effect of Price Changes on the Demand and Supply of Separable Goods” (in Japanese), Kokumin Keisai Zasshi 74, 1–51. {% Show that subjects prefer simple prospects more than complex ones. %} Sonsino, Doron, Uri Benzion, & Galit Mador (2002) “The Complexity Effects on Choice with Uncertainty—Experimental Evidence,” Economic Journal 112, 936–965. {% %} Sonsino, Doron, Mosi Rosenboim, & Tal Shavit (2015) “The Valuation of Composite Investment Instruments,” working paper. {% %} Sono, Masazo (1961) “The Effect of Price Changes on the Demand and Supply of Separable Goods,” International Economic Review 2, 239–271. {% free-will/determinism: subjects could at will push one of two buttons. Whenever they made a decision to do it they indicated so; however, brain activities showed the decision to come some 8 seconds before subjects said they took the decision. %} Soon, Chun Siong, Marcel Brass, Hans-Jochen Heinze, & John-Dylan Haynes (2008) “Unconscious Determinants of Free Decisions in the Human Brain,” Nature Neuroscience 11, 543–545. {% probability triangle. Test fanning out in probability triangle. Find that on the border it happens, but inside the triangle, EU is good. %} Sopher, Barry & Gary Gigliotti (1993) “A Test of Generalized Expected Utility Theory,” Theory and Decision 35, 75–106. {% Argue that observed intransitivities in Loomes, Starmer & Sugden is only random error. %} Sopher, Barry & Gary Gigliotti (1993) “Intransitive Cycles: Rational Choice or Random Error? An Answer Based on Estimation of Error Rates with Experimental Data,” Theory and Decision 35, 311–336. {% Seem to find evidence for quasi-convexity w.r.t. probabilistic mixing , supporting convex probability weighting in RDU. %} Sopher, Barry & J. Mattison Narramore (2000) “Stochastic Choice and Consistency in Decision Making under Risk: An Experimental study,” Theory and Decision 48, 323–349. {% decreasing/increasing impatience: find constant discounting real incentives/hypothetical choice: for time preferences: seems to be %} Sopher, Barry & Arnav Sheth (2006) “A Deeper Look at Hyperbolic Discounting,” Theory and Decision 60, 219–255. {% Newcomb’s problem; schrift p. 407 %} Sorensen, Roy A. (1983) “Newcombs Problem: Recalculations for the One-Boxer,” Theory and Decision 15, 399–404. {% %} Sosa, E. David (1993) “Consequences of Consequentialism,” Mind 102, 101–122. {% %} Sosonko, Genna & Paul van der Sterren (1998) “New in Chess Yearbook 46; The Grandmaster Guide to Openings.” Interchess BV, Alkmaar. {% %} Souchek, Julianne, James R. Stacks, Baruch Brody, Carol M. Ashton, R.Brian Giesler, Margaret M. Byrne, Karon Cook, Jane M. Geraci, Nelda P. Wray (2000) “A Trial for Comparing Methods for Eliciting Treatment Preferences from Men with Advanced Prostate Cancer,” Medical Care 38, 1040–1050. {% Discusses minsum functions; i.e., multiattribute utility functions that are constructed by min and addition operations, such as min{ x1,x2} + x3. %} Sounderpandian, Jayavel (1991) “Value Functions when Decision Criteria are not Totally Substitutable,” Operations Research 39, 592–600. {% Shows how theorem of Kolmogorov is of use for additive conjoint measurement. %} Sounderpandian, Jayavel (1992) “Transforming Continuous Utility into Additive Utility Using Kolmogorov’s Theorem,” Journal of Multi-Criteria Decision Analysis 1, 93–99. {% %} Sox, Harold C., Marshall A. Blatt, Michael C. Higgins, & Keith I. Marton (1986) “Medical Decision Making.” Buttersworths, Boston. {% %} Spalt, Oliver G. (2011) “Small Chances and Large Gains: Why Riskier Firms Grant more Employee Stock Options,” Dept. of Finance, Tilburg University, the Netherlands. {% foundations of probability: proposes an interpretation, discussing counterarguments such as circularity in definition and impossibility to assign probability to single events. %} Spanos, Aris (2013) “A Frequentist Interpretation of Probability for Model-Based Inductive Inference,” Synthese 190, 1555–1585. {% Studies aversion to compound lotteries, and relate it to wealth, in India and El Salvador. %} Spears, Dean (2013) “Poverty and Probability: Aspiration and Aversion to Compound Lotteries in El Salvador and India,” Experimental Economics 16, 263–284. {% small worlds idea? %} Spence, Michael & Richard J. Zeckhauser (1972) “The Effect of the Timing of Consumption Decisions and the Resolution of Lotteries on the Choice of Lotteries,” Econometrica 40, 401–403. {% utility measurement: correct for probability distortion: criticize Oliver (2005) for correcting only for loss aversion and not for probability transformation. %} Spencer, Anne, Judith Covey, Susan Chilton, & Matthew J. Taylor (2005) “Testing the Internal Consistency of the Lottery Equivalents Method Using Health Outcomes: A Comment to Oliver,” Health Economics 14, 161–167. {% Utility independence is mostly verified. %} Spencer, Anne & Angela Robinson (2007) “Tests of Utility Independence when Health Varies over Time,” Journal of Health Economics 26, 1003–1013. {% %} Spencer, Brue D. & Lincoln E. Moses (1990) “Needed Data Expenditure for an Ambiguous Decision Problem,” Journal of the American Statistical Association 85, 1099–1104. {% probability elicitation %} Spetzler, Carl S. & Carl-Axel S. Staël von Holstein (1975) “Probability Encoding in Decision Analysis,” Management Science 21, 340–358. {% probability elicitation; referaat Rene Eijkemans, april ’94 %} Spiegelhalter, David J. (1986) “Probabilistic Prediction in Patient Management and Clinical Trials,” Statistics in Medicine 5, 421–433. {% This paper seems to give an alternative justification for Jaffray’s updating rule. %} Spies, Marcus (1991) “Combination of Evidence with Conditional Objects and Its Application to Cognitive Modeling.” In Goodman, Irwing R. et al. (eds.) Conditional Logic in Expert Systems, North-Holland, Amsterdam. {% %} Spies, Marcus (1995) “Uncertainty and Decision Making - Expert Treatment of Human Expertise.” In Jean-Paul Caverni, Maya Bar-Hillel, Francis Hutton Barron, & Helmut Jungermann (eds.) Contributions to Decision Making—I, 51–79, Elsevier, Amsterdam. {% %} Spiliopoulos, Leonidas & Andreas Ortmann (2017) “The BCD of Response Time Analysis in Experimental Economics,” Experimental Economics, forthcoming. {% Published postuum. free-will/determinism: seems (wikipedia is my source) that Spinoza does not think that God is an outside power, or something personalized, but rather than God is everything and not personalized, which may not be far from my atheist view that God does not exist. Third part of Ethica (De Origine et Natura Affectuum - about the origin and nature of emotions) is relevant for decision theory, and the fifth part (De Potentia Intellectus, seu de Libertate Humana - about the power of mind; i.e., human free will). Seems that Spinoza takes the world as deterministic, but still sees a role for our free will. That it is something like confirmation of what will happen anyhow. We suffer from wrong ideas and get happy if right ideas. Every being wants to prolong its existence (sound like Darwin’s evolution) and will is where our mind is aware of us trying to do so. Gladness and sadness (positive and negative utility I economist would say) drive our actions/signal to us if actions are good. So there is no good or bad but just being closer to your real nature or not. %} Spinoza, Baruch (1678) Ethica. {% Doi: http://dx.doi.org/10.1016/j.jmateco.2012.09.005 %} Spinu, Vitalie & Peter P. Wakker (2013) “Expected Utility without Continuity: A Comment on Delbaen, Drapeau, and Kupper (2011)” Journal of Mathematical Economics 49, 28–30. Link to paper
Spitzer, Walter O., Annette J. Dobson, Jane Hall, Esther Chesterman, John Levi, Richard Shepherd, Renaldo N. Battista, Barry R. Catchlove (1981) “Measuring the Quality of Life of Cancer Patients,” Journal of Chronic Disease 34, 585–597. {% R.C. Jeffrey model: seems to argue that one cannot assign a probability to one’s own choice. %} Spohn, Wolfgang (1977) “Where Luce and Krantz Do Really Generalize Savage’s Decision Model,” Erkenntnis 11, 113–134. {% Newcomb’s paradox %} Spohn, Wolfgang (2012) “Reversing 30 Years of Discussion: Why Causal Decision Theorists Should One-Box,” Synthese 187, 95–122. {% This paper measures probability equivalents (PEs) and certainty equivalents (CEs), finding more risk aversion for PEs, as this has been found in many studies since the 1980s. But the experiment in this paper has been done particularly carefully, with within- and between-subject comparisons, many controls, and of course real incentives, as is common by the high experimental standards of experimental economics. The paper makes the interesting observation that this finding can be explained by the Köszegi-Rabin model, if we make the plausible assumption that in PE question the certain outcome is chosen as reference point and in the CE question the lottery. Under reference dependence, the reference outcome is favored relative to others (whose cons are overweighted and pros are underweighted) and, hence, choosing the lottery as reference point, as in CE questions, brings more preference for the lottery and more risk seeking. The observation similarly holds for any theory that allows the (noncertain) lottery to be a reference point, such as the PT3 theory cited for this by the author. I have two difficulties with this paper: (1) P. 1463 last para claims that prospect theory would assume the same reference point for CE as for PE and, hence, would be violated by the discrepancy between PE and CE, but this is absolutely not true. Bleichrodt, Pinto, & Wakker (2001 Management Science; received the Decision Analysis Society Publication award of 2003) gives detailed experimental and numerical analyses showing that prospect theory can explain the discrepancies between PE and CE because it assumes different reference points here. It shows that this works for the commonly found parameters for PT. (2) The paper cites some initial papers that reported the CE-PE discrepancy before in the early 1980s, but only does so at the back, p. 1494 2nd para. I am glad that the author, unlike experimental economists such as Holt & Laury (2002), took note of papers written by others than experimental economists. But it would have been better had this work been cited up front to show to the readers that the discrepancy reported here is not new. Also there is much more literature on this, with satisfactory explanations or the discepancy available. If I may start with papers written by my students, besides the paper cited under (1), there is Bleichrodt (2002 Health Economics) who offers a careful explanation of the discrepancy using prospect theory, and van Osch, van den Hout, & Stiggelbout (2006) who let subjects do speak-aloud to investigate what reference points they used. For other literature, my bibliography here, using the term standard gamble (SG) iso PE, has some key words: SG doesn’t do well, SG higher than CE, SG higher than others, CE bias towards EV, giving some 40 references on the topic. The author calls the topic a “long-standing issue” in the literature and writes: “The present results and use of the KR model may help to resolve this longstanding issue.” This underestimates the explanations provided before. This problem is not very serious. This paper has provided careful evidence, a valuable new explanation, and has cited more preceding literature than sometimes done by experimental economists. %} Sprenger, Charles (2015) “An Endowment Effect for Risk: Experimental Tests of Stochastic Reference Points,” Journal of Political Economy 123, 1456–1499. {% foundations of statistics %} Sprenger, Jan (2009) “Statistics between Inductive Logic and Empirical Science,” Journal of Applied Logic 7, 239–250. {% three-prisoners problem; criticizes Baumann and defends the commonly accepted solution, defending the relevance of probability theory in single cases. %} Sprenger, Jan (2010) “Probability, Rational Single-Case Decisions and the Monty Hall Problem,” Synthese 174, 331–340. {% inverse-S?; argues so on the basis of French, Spanish, and Mexican lotteries. %} Sprowls, R. Clay (1953) “Psychological-Mathematical Probability in Relationships of Lottery Gambles,” American Journal of Psychology 66, 126–130. {% %} Sprumont, Yves & Lin Zhou (1999) “Pazner-Schmeidler Rules in Large Societies,” Journal of Mathematical Economics 31, 321–339. {% %} Spurrier, Michael & Alexander Blaszczynski (2014) “Risk Perception in Gambling: A Systematic Review,” Journal of Gambling Studies 30, 253–276. {% probability elicitation %} Staël von Holstein, Carl-Axel S. (1972) “Probabilistic Forecasting: An Experiment Related to the Stock Market,” Organizational Behaviour and Human Performance 8, 139–158. {% The author replicates the Ellsberg tasks. He finds much noise in the data, and a bit ambiguity aversion. In the Ellsberg task, a coin toss decides what the winning color is, thus à la Raiffa (1961) explicitly making the ambiguous option quite a 0.5 probability option. May be inspired by the theoretical literature on ambiguity, he assumes EU and even EV for risk. %} Stahl, Dale O. (2014) “Heterogeneity of Ambiguity Preferences,” Review of Economics and Statistics 96, 609–617. {% Safe is a journal for clients of Robeco investment Engineers and the Rabobank. %} Stallinga, Rob & Peter P. Wakker (2013) “Wie nooit Wil Verliezen, Mist veel Kansen,” Safe 2013#02, p. 26. Link to paper
Stalmeier, Peep F.M. (2002) “Discrepancies between Chained and Classic Utilities Induced by Anchoring with Occasional Adjustments,” Medical Decision Making 22, 53–64. {% risky utility u = strength of preference v (or other riskless cardinal utility, often called value): this paper gives beautiful support for the hypothesis that risky utility = riskless utility. Measure utility, of health outcomes (# days migraine), through direct strength-of-preference and through CE (certainty equivalent). Correction for probability transf. reconciles partly but not completely, CE utility remains more concave. They propose that this is caused by framing + loss aversion. They then strongly frame outcomes as losses so that loss aversion plays no more role. In the latter case, indeed, the discrepancy between risky and riskless utility disappears. They let participants write down probabilities and outcomes in a figure to verify that the participants took notice of probabilities/outcomes. Do few participants (8 + 6), but very thorough treatment, several session, hours, repeated measurements, of each participant, videos to show the participants effects of migraine etc. inverse-S: they find that probability weighting is inverse-S. P. 19 bottom of version of October 1998: “Thus, it appears that a prescriptive choice needs to be made as to which framing effect is desired …” Seems to find, as do Hershey & Schoemaker (1982), that in standard gamble choices people focus on the sure outcome as their reference point. %} Stalmeier, Peep F.M. & Thom G.G. Bezembinder (1999) “The Discrepancy between Risky and Riskless Utilities: A Matter of Framing?,” Medical Decision Making, 19, 435–447. {% %} Stalmeier, Peep F.M., Thom G.G. Bezembinder, & Ivana J. Unic (1996) “Proportional Heuristics in Time Tradeoff and Conjoint Measurement,” Medical Decision Making 16, 36–44. {% %} Stalmeier, Peep F.M., Peter P. Wakker, & Thom G.G. Bezembinder (1997) “Preference Reversals: Violations of Unidimensional Procedure Invariance,” Journal of Experimental Psychology, Human Perception and Performance 23, 1196–1205. Link to paper
Stalpers, Lucas J.A. (1991) “Clinial Decision Making in Oncology,” Ph.D. thesis, Institute of Radiotherapy, University of Nijmegen, Nijmegen, the Netherlands. {% %} Stalpers, Lucas J.A., & Arne Maas (1991) “Utiliteitsmeting met Behulp van Additief Conjunct Meten ten Behoeve van the Klinische Besluitvorming,” Nederlands Tijdschrift voor de Psychologie 46, 139–145. {% Show the exponential growth bias: people do not understand how quickly constant discounting weights become smaller over time and, hence, overestimate the future discount factors. This can be one explanation of decreasing impatience. %} Stango, Victor & Jonathan Zinman (2009) “Exponential Growth Bias and Household Finance,” Journal of Finance 64, 2807–2849. {% Seems to be good book on Möbius inverse. %} Stanley, Richard P. (1986) “Enumerative Combinatorics. Vol. I” Wadsworth & Brooks/Cole, Monterey, CA. {% real incentives/hypothetical choice: uses random incentive system; violation of certainty effect: set 1, Questions 4 and 1 give it. For five probabilities not denoted here, the paper considers choices between S = (c,b,b,b,a) and R = (c,c,b,a,a) for outcomes c b a. Thus, it can test all kinds of violations of (comonotonic) independence within the probability triangle. This study was done more or less simultaneously with Camerer (1989), but the processing/rewriting with RESTUD went slowly. P. 817: I do not understand the choice of A=0 for PT. Paper tests PT only for convex probability weighting w, not for inverse-S for instance. P. 818 top erroneously suggests that Kahneman & Tversky (1979) had suggested that w be convex. This is a widespread misunderstanding. Tversky told me that they drew their 1979 curve loosely by hand, and that people paid too much attention to the particular shape in the middle. The convexity in the middle indeed is not at all pronounced or important, but the jumps at 0 and 1 are. The jump at p = 0 entails a violation of convexity. %} Starmer, Chris (1992) “Testing New Theories of Choice under Uncertainty Using the Common Consequence Effect,” Review of Economic Studies 59, 813–830. {% Considers approach where subjects do not maximize a transitive preference, but based on some cognitive dissonance model. Pp. 185-186 discuss the Shackle model. %} Starmer, Chris (1993) “The Psychology of Uncertainty in Economic Theory: A Critical Appraisal and a Fresh Approach,” Review of Political Economy 5, 181–196. {% Constructive view of preference. Presented at the conference on Incommensurability and Value in Caen, April 1994. %} Starmer, Chris (1996) “Explaining Risky Choices without Assuming Preferences,” Social Choice and Welfare 13, 201–213. {% %} Starmer, Chris (1997) “The Economics of Risk.” In Peter Callow (ed.) The Handbook of Environmental Risk Assessment and Management, Ch. 12, 319–344, Blackwell, Oxford. {% P. F5: “Like it or not, economists have a bad reputation for being relatively unmoved by facts about the world.” P. F7: “Good news it seems, but here is the rub: further testing suggests that regret theory is not the correct explanation for the new phenomena whose discovery it prompted.” Paper ends with suggesting that maybe in the end economics and market-behavior is not seriously affected by all the biases that empirical studies in the lab find, but that, at present, we do not know and that, therefore, we should continue to investigate these things. %} Starmer, Chris (1999) “Experimental Economics: Hard Science or Wasteful Tinkering,” Economic Journal 109, F5–F15. {% Pp. 1-2: many nice citations of people arguing that controled experiments are difficult in economics. Argues for the usefulness of experimental economics. %} Starmer, Chris (1999) “Experiments in Economics: Should We Trust the Dismal Scientists in White Coats?,” Journal of Economic Modeling 6, 1–30. {% real incentives/hypothetical choice: uses random incentive system; PT falsified: when OPT (1979-prospect theory) predicted particular violations of transitivity and monotonicity (if no editing), the theory was widely criticized for it. This paper, however, tests such violations of transitivity (or monotonicity) and finds them confirmed. It, thus, gives empirical support to OPT. Details: Prospect A = 140.200; Prospect B = 80.300; Prospect C = (0.15:8, 0.15:7.75, 0.70:0). By monotonicity, B C, but by subadditivity of probability weighting under OPT (which amounts to event-splitting effect; coalescing) we can have C B. OPT predicts C A B (including C B) because the evaluating function implies these prefs. It, however, predicts B C because of monotonicity and editing, and thus intransitivity results. Testing number of cycles C A B C versus number of reversed cycles C A B C would not be very satisfactory because simple error theories could predict fewer errors in B C because of salience of monotonicity, and thus predominance of former cycles, without genuine intransitivity underlying it. This paper, therefore, tests only frequency of A C versus A B, and finds the former dominating. This is enough, under any plausible error theory, to ensure that either monotonicity or transitivity must be violated. Data find few violations of monotonicity and, hence, transitivity must be violated. These data were found for many stimuli A,B,C similar to the above ones. %} Starmer, Chris (1999) “Cycling with Rules of Thumb: An Experimental Test for a New Form of Non-Transitive Behavior,” Theory and Decision 46, 141–158. {% survey on nonEU; P. 348 1st para: drawback of rank-dependence is drastic change of decision weight when rank-ordering changes, and no change at all otherwise. %} Starmer, Chris (2000) “Developments in Non-Expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk,” Journal of Economic Literature 38, 332–382. {% Well-organized and accessible discussion of the normative/descriptive debate about the Allais paradox, with nice references and citations, focusing on Friedman & Savage (1948) arguments. Starmer argues that normative appeal need not imply descriptive plausibility. P. 297 bottom: this paper takes EU axioms as normatively appealing, only for the sake of argument. Pp. 281-282 give the formula SUMw(pj)U(xj) as “This is essentially the type of value function assumed in prospect theory of Kahneman and Tversky (1979)”. For two-nonzero-outcome prospects K&T79 used a different formula, and there have been many misunderstandings about it. P. 287 has Raiffa argument that prescriptive theory would have nothing to offer if no descriptive violations. On two points I disagree with the author. 1. We may be DESCRIPTIVELY interested in the behavior and preferences of people only at a level of thinking where, what we have chosen to designate as elementary mistakes, are avoided. (Starmer calls our choosing a precommitment to a descriptive viewpoint.) We may think that preferences and value system are per definition transitive so that, if we observe a violation, it is a mistake and not preference or value. This point is propagated by many experimental economists. Then normative considerations do enter a purely DESCRIPTIVE enterprise. Savage did Allais paradox upon first acquaintance but not after thinking. If we want to know descriptively what Savage would do from some time in history on, then it is: not violating EU in the Allais paradox! 2. I think that normative status of something does make it empirically plausible. Only in very exceptional situations such as the Allais paradox are what I consider mistakes likely to arise and a majority may deviate from what is normative. This is a very exceptional situation that does not invalidate the descriptive plausibility implied by a normative status. Starmer seems to impicitly focus his attention to those very exceptional situations. %} Starmer, Chris (2005) “Normative Notions in Descriptive Dialogues,” Journal of Economic Methodology 12, 277–289. {% real incentives/hypothetical choice: random incentive system, explained on p. 93; this is same experiment as their 1989 JRU paper, so see there for further explanation. PT falsified: they find a necessary condition of PT and RDU violated. The necessary condition, explained on pp. 86-90, was found by accident (explained on p. 95 bottom), but actually is really clever. Define the cumulative prospect theory functional (so rank- and sign-dependent utility) for decision under risk, in the appendix. Preceded Tversky & Kahneman (1992) and Luce & Fishburn (1991). Well, they don’t take a general probability transformation for losses but the dual of the one for gains (as reflection would have it), but still it is clear that the rank- and sign-dependent idea is there. This paper was, in turn, preceded by Šipoš (Sipos) (1979) who also defines the symmetrical integral. %} Starmer, Chris & Robert Sugden (1989) “Violations of the Independence Axiom in Common Ratio Problems: An Experimental Test of Some Competing Hypotheses,” Annals of Operations Research 19, 79–102. {% coalescing; Download 7.23 Mb. Share with your friends:
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