Bibliography
conservation of influence
Download 7.23 Mb.
|
- Navigate this page:
- Risk averse for gains, risk seeking for losses
- PT falsified
- PT: data on probability weighting ; inverse-S
conservation of influence: several references to psychological/philosophical literature on will. %} Wolozin, Harold (2002) “The Individual in Economic Analysis: Toward Psychology of Economic Behavior,” Journal of Socio-Economics 31, 45–57. {% Newcomb’s paradox %} Wolpert , David H. & Gregory Benford (2013) “The Lesson of Newcomb’s Paradox,” Synthese 190, 1637–1646. {% %} Womack, Andrew J., Luis León-Novelo, & George Casella (2014) “Inference from Intrinsic Bayes’ Procedures under Model Selection and Uncertainty,” Journal of the American Statistical Association 109, 1040–1053. {% ordering of subsets: characterization of qualitative orderings of finite algebras that can be represented by belief functions (complicated proof). Drawback is that the functions are mostly unique only up to an ordinal transformation, given the absence of additivity as probability measures. Roughly, any weak ordering of a finite algebra satisfying monotonicity w.r.t. set inclusion and one more kind of null invariance condition (with >´ denoting strict preference) (A >´ B and AC = then AC >´ BC) seems to be representable by a belief function if I understand right. Idea is to start with a quantitative representation whatsoever and then apply a sufficiently concave transformation to get all inequalities satisfied. Main theorem briefly described by Mukerji (1997) Economic Theory. %} Wong, S.K. Michael, Yi Yu Yao, Peter Bollmann, & H.C. Bürger (1991) “Axiomatization of Qualitative Belief Structure,” IEEE Transactions on Systems, Man, and Cybernetics 21 (4) 726–734. {% An original way to measure the interesting differences between dynamic consistency, naivity, and sophistication. Students are asked: (a) How much time spent on studying a course to be taken in the future would be optimal; (b) how much time they expect to actually study it; (c) afterwards how much they really studied. (a) = (c) is time consistent. If (a) (c), then (b) = (a): naïve; (b) = (c): sophisticated. (b) in between is partially sophisticated. My main problem: (a) (c) can be due to unforeseen circumstances, rather than time inconsistency. The author argues (p. 546 end of 2nd para) that such unforeseen circumstances, if random and exogenous, are only noise and generate no bias, but I disagree: their average is not 0, but positive. This is typical of time planning, as considered here: they are usually underestimations because unforeseen things are usually bringing extra delays. Would have been interesting had the author asked a question at (c) if there had been unforeseen circumstances, and how big they were. P. 646 3rd para says that it is surprising that predicted delay in one sample has worse general performance than unpredicted delay, but this can be explained by the problem mentioned, that unpredicted delay can be clever students subject to unforeseen extraneous delays. (b) (c) is an index of lack of self-control. Question is also to what extent the subjects have an interest in truthfully responding, but I cannot easily think of biases. DC = stationarity: p. 646 3rd l of §2.1 writes that time consistency iff exponential discounting. %} Wong, Wei-Kang (2008) “How Much Time-Inconsistency Is there and Does It Matter? Evidence on Self-Awareness, Size, and Effects,” Journal of Economic Behavior and Organization 68, 645–656. {% on bookmakers, bettors %} Woodland, Bill M. (1991) “The Effects of Risk Aversion on Wagering: Point Spread versus Odds,” Journal of Political Economy 99, 638–653. {% %} Woolfolk, Robert L. & Louis A. Sass (1988) “Behaviorism and Existentialism Revisited,” Journal of Humanistic Psychology 28, 108–119. {% Proposes a theory of subjective perception (elaborated in detail in a working paper) where perception depends on calculating capacity available and expectation of distribution of stimuli in environment, which reminds me of the range-frequency theory of Parducci and decision by sampling by Chater, Stewart, and others. It leads to reference dependence where the reference point is the expectation as in Köszegi & Rabin, and risk aversion for gains with risk seeking for losses (Risk averse for gains, risk seeking for losses). %} Woodford, Michael (2012) “Prospect Theory as Efficient Perceptual Distortion,” American Economic Review, Papers and Proceedings 102, 41–46. {% foundations of statistics %} Worrall, John (2007) “Why There's No Cause to Randomize,” British Journal for the Philosophy of Science 58, 451-488. {% ratio-difference principle: seems to have shown that consumer judgment of a price difference can be modeled as a weighted average of price ratio and price difference. %} Wright, John H. (1998) “Constructing Price Differences,” working paper, Center for Decision Research, The University of Chicago. {% %} Wright, Patricia, & Daniel Kahneman (1971) “Evidence of Alternative Strategies of Sentence Retention,” Quarterly Journal of Experimental Psychology 23, 197–213. {% %} Wright, Peter (1974) “The Harassed Decision Maker: Time Pressures, Distractions, and the Use of Evidence,” Journal of Applied Psychology 59, 555–561. {% probability elicitation %} Wright, William F. (1988) “Empirical Comparison of Subjective Probability Elicitation Methods,” Contemporary Accounting 5, 47–57. {% Find neural basis for skewness preference; i.e., preference for positive skew and against negative skew. This is equivalent to inverse-S probability weighting. The authors, on p. 1 top of 2nd column, incorrectly claim that this is not so, citing incorrect claims by Levy & Levy (2004). %} Wu, Charlene C., Peter Bossaerts, & Brian Knutsen (2011) “The Affective Impact of Financial Skewness on Neural Activity and Choice,” Plos ONE 6, e16838. {% %} Wu, George (1993) “Temporal Risk and Probability Weights: Rank-, Sign-, and Timing-Dependent Utility,” Harvard Business School, Boston MA. {% real incentives: not used; instead, flat payment Structure on p. 42, with r = q´-q, and s remaining probability. R S p q r s p q r s x y 0 0 x y´ y´ 0 y y 0 0 y y´ y´ 0 B question PT falsified through coalescing; inverse-S: finds violations of PT due to cancelling of common outcomes, that original prospect theory can account for; taking that as given, probability weighting seems to be inverse-S; %} Wu, George (1994) “An Empirical Test of Ordinal Independence,” Journal of Risk and Uncertainty 9, 39–60. {% %} Wu, George (1996) “The Strengths and Limitations of Expected Utility Theory,” Medical Decision Making 16, 9–10. {% %} Wu, George (1999) “Anxiety and Decision Making with Delayed Resolution of Uncertainty,” Theory and Decision 46, 159–198. {% PT: data on probability weighting; inverse-S of weighting function; §5 does estimations; use preference ladders, which means choices that differ only regarding their common outcome. real incentives: they used flat payments decreasing ARA/increasing RRA: use power utility; x0.55 comes out as utility function for gains. %} Wu, George & Richard Gonzalez (1996) “Curvature of the Probability Weighting Function,” Management Science 42, 1676–1690. {% coalescing %} Wu, George & Richard Gonzalez (1996) “Dominance Violations and Event Splitting,” School of Business, Harvard University, Boston, MA. {% PT: data on probability weighting; inverse-S of weighting function %} Wu, George & Richard Gonzalez (1998) “Common Consequence Conditions in Decision Making under Risk,” Journal of Risk and Uncertainty 16, 115–139. {% PT: data on probability weighting; inverse-S of weighting function real incentives: they used flat payments,. %} Wu, George & Richard Gonzalez (1999) “Nonlinear Decision Weights in Choice under Uncertainty,” Management Science 45, 74–85. {% %} Wu, George, Chip Heath, & Richard P. Larrick (2001) “A Value Function-Based Model of Goal Behavior,” {% PT falsified: the authors claim that the weighting function for mixed prospects is less sensitive than that for pure gains or pure losses (probability weighting depends on outcomes). However, they don’t have enough data to separate curvature from elevation (they assume only one weighting parameter that captures both) and also cannot separate it from loss aversion. %} Wu, George & Alex B. Markle (2008) “An Empirical Test of Gain-Loss Separability in Prospect Theory,” Management Science 54, 1322–1335. {% Test OPT (’79 version of prospect theory) versus PT (or CPT; ’92 version of prospect theory). Overall, OPT does some better. Unfortunately, it is not clear in many discussions if OPT in the authors’ terminology refers to exactly OPT of ’79 (which the authors often describe as “OPT with an editing operation”) or to the different formula that they write in Eq. 1.2. They assume at most three outcomes, the domain where OPT is defined only, but which gives an advantage to OPT because its natural extension to more outcomes does not work at all. no real incentives but flat payment. Strangely enough, Eq. 1.2 says that OPT(p1:x1,p2:x2) = (p1)v(x1) + (p2)v(x2), also if p1 + p2 = 1. (*) The formula OPT(p1:x1,p2:x2) = v(x2) + (p1)(v(x1) – v(x2)) for p1 + p2 = 1 and x1 > x2 (**) they call OPT “if editing.” However, (**) !is! OPT, and (*) !is not! OPT. See Kahneman & Tversky (1979, Eq. 2). They derive a tradeoff consistency condition for PT, based on Abdellaoui (2002), and one derived here for their version of OPT (Eq. *), and find data in the probability triangle where these two give contradictory predictions. violation of certainty effect: p. 120 reports that Simplex IV gives, strangely enough, a violation of the certainty effect. P. 126 writes that PT has several advantages so that “Thus, our tests should not be seen as reason to abandon PT.” %} Wu, George, Jiao Zhang, & Mohammed Abdellaoui (2005) “Testing Prospect Theories Using Tradeoff Consistency,” Journal of Risk and Uncertainty 30, 107–131. {% inverse-S: in a motor task, subjects had to quickly hit a spot on a screen and then got prizes if they succeeded. After some learning, their hit probabilities stabilized (the subjects were not told what these were but could experience). Then they were given choices between different games, which amounts to choices between different lotteries. They also answered traditional risky decision questions. In motor decision tasks people are closer to EU than in usual decision tasks (several further references are given). The utility functions elicited were the same (source-dependent utility: not the case here), but the probability weighting functions were different, with motor tasks giving the opposite of inverse-S. The motor task is very similar to the experienced decision tasks studied by Erev, Hertwig, and others, involving some ambiguity, be it that now motoric skills come in. Note here that a crucial assumption in Savage’s (1954) expected utility is that the decision maker has no influence at all on the states of nature (no moral hazard). An explanation may be that subjects dislike a task where they fail with high probability. Another difference with classical decisions under risk is that the motoric task has repeated payments, so perceptions of laws of large nrs. come in. %} Wu, Shih-Wei., Mauricio R. Delgado, & Laurence T. Maloney (2009) “Economic Decision-Making under Risk Compared with an Equivalent Motor Task,” Proceedings of the National Academy of Sciences 106, 6088–6093. {% Rescale EQ-5D using VAS. %} Wu, Xiuyun, Arto Ohinmaa, Jeffrey A. Johnson, & Paul J. Veugelers (2014) “Assessment of Children’s Own Health Status Using Visual Analogue Scale and Descriptive System of the EQ-5D-Y: Linkage between Two Systems,” Quality of Life Research 23, 393–402. {% ordering of subsets %} Wynn, Henry P. (1983) “Optimum Subset Problems in Statistics and Operations Research.” In Simon French (Ed.), Multi-Objective Decision Making, Academic Press, New York, 49–58. {% Does maxmin, but not mins over EUs, but mins over CEU (Choquet expected utility)s. Thus it provides a common generalization of Cerreia et al. (2011 JET) and Schmeidler (1989). He uses the Anscombe-Aumann (AA) model, at least assumes a convex outcome set with linear utility there. %} Xia, Jianming (2013) “Comonotonic Convex Preferences,” working paper. {% Do what title says, with intertemporal growth also considered. Get a CCAPM model for RDU. One restriction they need is that all agents have the same probability weighting. Section 7 shows that their RDU results can be translated into EU results with a modified utility function, and end of Section 7 derives rank-neutral probabilities. This sheds some role on risk aversion in combination with as-if risk-neutral, something in finance that has puzzled me. %} Xia, Jianming & Xun Yu Zhou (2016) “Arrow–Debreu Equilibria for Rank-Dependent Utilities,” Mathematical Finance 26, 558–588. {% The value heuristic entails that people use extremity of value as a cue to expect low frequency. %} Xianchi Dai, Klaus Wertenbroch, & C. Miguel Brendl (2008) “The Value Heuristic in Judgments of Relative Frequency,” Psychological Science 19, 18–19. {% measure of similarity %} Xiao, Jitian & Yanchun Zhang (2001) “Clustering of Web Users Using Session-Based Similarity Measures,” Proceedings of International Conference on Computer Networks and Mobile Computing, 223–228. {% utility families parametric; Seems to propose his family as improvement of Merton’s HARA. His family seems to be the same as Saha’s expo-power family, with Xie’s one minus a parameter of Saha and Xie’s the product of the two parameters of Saha. Xie’s power risk aversion family seems to be , with 0 and 0. U´´/U´ = /x + x. %} Xie, Danyang (2000) “Power Risk Aversion Utility Function,” Annals of Economics and Finance 1, 265–282. {% %} Xiong, Wei, Xudong Luo, Wenjun Ma, & Minjie Zhang (2014) “Ambiguous Games Played by Players with Ambiguity Aversion and Minimax Regret,” Knowledge-Based Systems 70, 167–176. {% Implement Dempster-Shafer so as to avoid the problem of assigning prior probabilities. %} Xu, Yejun, Kevin W. Li, & Huimin Wang (2013) “Dempster Shafer Neural Network Algorithm for Land Vehicle Navigation Application,” Information Sciences 253, 56–73. {% A remarkable paper that contains many of the ideas basic to prospect theory! utility elicitation: one of the few empirical papers actually trying to find out whether gambles for money show risk aversion through an experiment. Takes DUU with finite state space and monetary outcomes. Explains that in SEU the probabilities are not objectively given and therefore traditional risk aversion cannot be defined. Then tests convexity of prefs. Does not show formally that that is equivalent to risk aversion in DUU. The tests of convexity are such that they involve, by modern views, loss aversion, which may explain the extensive risk aversion = convexity found there. inverse-S: end of §IV finds longshot effect, and explains it by overestimation of small probability rather than by EU. real incentives: it seems that he used that. He discusses an auction and the random incentive system to do so, and suggests that these were done, but is not 100% clear on it. P. 278: “because utility and probability are two purely theoretical components of an integral decision process.” P. 281, in criticism of Friedman & Savage (1948), Yaari confuses risky and riskless utility. P. 282: “writing P ... for preference or indifference, and agreeing to call the wealth level to which the relation corresponds the zero wealth level. In other words, let us agree to measure wealth in terms of deviations from the level which corresponds to P. P. 285 2nd para: discusses that each choice should be in isolation, and in fact proposes RIS, where unfortunately he also suggests that maybe a few, so more than one, choices will be implemented. The description of the experiment does not make clear how the incentives were actually implemented. End of §IV finds that several participants (seven out of seventeen) exhibited risk seeking for small probability inverse-S: Yaari posits this on p. 290: “one finds that some subjects tend to overstate low probabilities and to understate high probabilities” and refers to Preston & Baratta (1948) and Mosteller & Nogee (1951) for related findings. %} Yaari, Menahem E. (1965) “Convexity in the Theory of Choice under Risk,” Quarterly Journal of Economics 79, 278–290. {% Seems to have mentioned that discounting can be due to uncertainty. %} Yaari, Menahem E. (1965) “Uncertain Lifetime, Life Insurance, and the Theory of Consumer,” Review of Economic Studies 32, 137–150. {% Introduced comonotonicity on p. 328 (“bets on the same event,” also stated for n events) but did, obviously, not foresee its role in nonadditive theories. When Yaari worked on his (1987, Econometrica) paper on rank-dependent theories, he first was not aware of the role of comonotonicity. He learned it from Schmeidler. Hence, I still think it is fair to say that Schmeidler invented comonotonicity for rank-dependent theories. %} Yaari, Menahem E. (1969) “Some Remarks on Measures of Risk Aversion and on Their Uses,” Journal of Economic Theory 1, 315–329. {% %} Yaari, Menahem E. (1977) “A Note on Separability and Quasi-Concavity,” Econometrica 45, 1183–1186. {% %} Yaari, Menahem E. (1978) “Separable Concave Utilities or the Principle of Diminishing Eagerness to Trade,” Journal of Economic Theory 18, 102–118. {% %} Yaari, Menahem E. (1984) “Risk Aversion without Diminishing Marginal Utility and the Dual Theory of Choice under Risk.” Research memorandum 65, Hebrew University, Jerusalem. {% Dutch book; P. 5 suggests that continuity has no empirical content. %} Yaari, Menahem E. (1985) “On the Role of “Dutch Books” in the Theory of Choice under Risk,” 1985 Nancy L. Schwartz memorial lecture, J.L. Kellogg Graduate School of Management, Northwestern University {% risky utility u = strength of preference v (or other riskless cardinal utility, often called value): suggests so. Says that risk aversion is attitude towards risk, and marginal utility towards wealth. He nowhere commits to EU or nonEU in a normative sense. %} Yaari, Menahem E. (1987) “The Dual Theory of Choice under Risk,” Econometrica 55, 95–115. {% %} Yaari, Menahem E. (1987) “Univariate and Multivatiate Comparisons of Risk Aversion: a New Approach.” In Walter P. Heller, Ross M. Starr, & David A. Starrett (eds.) Uncertainty, Information and Communication, Essays in Honor of Kenneth J. Arrow, Vol. III, 173–187, Cambridge University Press, Cambridge. {% %} Yaari, Menahem E. (1988) “A Controversial Proposal Concerning Inequality Measurement,” Journal of Economic Theory 44, 381–397. {% Proposed j=1;n (wj vj) where v1 ... vn and the wjs are weights, summing to one. That is, a symmetric case of the Choquet integal %} Yager, Ronald R. (1988) “On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decisionmaking,” IEEE Transactions on Systems, Man, and Cybernetics 18, 183–190. {% %} Yager, Ronald R. (1991) “Connectives and Quantifiers in Fuzzy Sets,” Fuzzy Sets and Systems 40, 39–75. {% %} Yager, Ronald R. & Liping Liu (2008) “Classic Works of the Dempster-Shafer Theory of Belief Functions.” Springer, Berlin. {% People find a 1286 out of 10,000 risk of cancer as higher than a 24.14 out of 100 risk. %} Yamagishi, Kimihiko (1977) “When a 12.86% Mortality is More Dangerous than 24.14%: Implications for Risk Communication,” Applied Cognitive Psychology 11, 495–506. {% On support theory. Binary complementarity can be violated if event has both many similarities and many dissimilarities with the conditioning event. %} Yamagishi, Kimihiko (2002) “Proximity, Compatibility, and Noncomplementarity in Subjective Probability,” Organizational Behavior and Human Decision Processes 87, 136–155. {% %} Yamagishi, Kimihiko & John M. Miyamoto (1996) “Asymmetries in Strength of Preference: A Focus Shift Model of Valence Effects in Difference Judgments,” Journal of Experimental Psychology: Learning, Memory, and Cognition 22, 493–509. {Formalizes uniqueness of utility and then analyzes which can escape from Arrow’s impossibility. %} Yamamura, Hirofumi (2017) “Interpersonal Comparison Necessary for Arrovian Aggregation,” Social Choice and Welfare 49, 37–64. {% Mainly discusses mass versus density/number of atoms and circularity in that. %} Yan, Kangnian (1990) “A Re-Examination into Newton’s Definition of Mass and Mach’s Criticism,” Historia Scientiarum 40, 29–39. {% The authors compare ambiguity with two-stage risk, applying two-stage ambiguity theories such as multiple priors (although they have no 2nd order distribution) and the smooth model to the latter. Then the predictions of the two-stage ambiguity models are violated. This provides evidence supporting non-two-stage models, for which the authors cite source preference of Tversky and others. %} Yang, Chun-Lei & Lan Yao (2017) “Testing Ambiguity Theories with a Mean-Preserving Design,” Quantitative Economics 8, 219–238. {% Multiattribute measurement of utility over time and money. The novelty of this paper is in a new optimization algorithm. %} Yang, I-Tung (2008) “Utility-Based Decision Support System for Schedule Optimization,” Decision Support Systems 44, 580–594. {% Considers ambiguity in games, but the ambiguity is only about nature’s moves (“external”). They show existence of equilibria, continuity in how they depend on ambiguity aversion. The paper does consider some ambiguity seeking, although no insensitivity. %} Yang, Jian (2018) “Game-Theoretic Modeling of Players’ Ambiguities on External Factors,” Journal of Mathematical Economics 75, 31–56. {% %} Yang, Jingni & Peter P. Wakker (2017) “Generalizing Many Theorems on Concave/Convex Utility or Weighting Functions,” working paper. {% They analyze how particularities of prospect theory can and cannot explain particular henomena, such as negative-feedback trading patterns. loss aversion: erroneously thinking it is reflection: I was glad to see that, unlike many authors in finance, these authors define loss aversion properly, and do not confuse it with reflection. %} Yao, Jing & Duan Li (2013) “Prospect Theory and Trading Patterns,” Journal of Banking & Finance 37, 2793–2805. {% Survey many (83), though obviously not all (Harless & Camerer 1994; Hey & Orme 1994), empirical studies into violations of EU. They do not really do a meta analysis, but they only list references. %} Yaqub, Muhammad Z., Gökhan Saz, & Dildar Hussain (2009) “A Meta Analysis of the Empirical Evidence on Expected Utility Theory,” European Journal of Economics, Finance and Administrative Sciences 15, 117–133. {% %} Yates, J. Frank (1982) “External Correspondence: Decompositions of Mean Probability Scores,” Organizational Behavior and Human Decision Processes 43, 145–171. {% probability elicitation; substitution-derivation of EU; Pp. 25-27 are on matching probabilities. P. 99: references to studies showing that overconfidence in lay judgment is not universal. For easy questions (extremely high probabilities) underconfidence Marcel zegt that Yates voordelen van PT groter vindt dan nadelen. Ch. 1, Ch. 2 up to p. 20, and Chs. 8-11 are on general decision, EV,EU, PT, etc. Rest of Ch. 2 and Chs. 3-7 are on probability elicitation. Chs. 12 etc. are on underlying psychological principles. Download 7.23 Mb. Share with your friends:
|