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§2: nice simple summary of original prospect theory
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| §2: nice simple summary of original prospect theory;
P. 24 point (ii) points out that shifting reference point (à la Shalev) has problems with PT’s assumption that U is concave above the reference point and convex below: “Note that the utility function for gains and losses cannot be s-shaped with respect to a moving reference point. To see this consider an interval [x1, x2], x2 > x1. Now if w is the initial wealth and w + x2 is the reference point then u(w + x), x1 x x2 is convex; whereas within the same range it is concave if w + x1 is the reference point. The inconsistency between the utility function for final wealth and the induced utility function for gains and losses does not occur if a person's utility function is exponential or linear.” P. 28 contains a nice method of eliciting utility for OPT nonparametrically Parametric utility elicitation is by taking exponential utility (p. 26: “as is traditionally done”) P. 26: “By choosing simple scenarios, we have conveniently avoided the complications of the editing phase” risky utility u = strength of preference v (or other riskless cardinal utility, often called value): on p. 28 they suggest that the value function of prospect theory is a riskless utility function (where “certainty method” is a kind of direct rating): “The value function in the prospect model can either be assessed by a certainty method or by a gamble method. We employed both methods even though we believe the certainty method is more desirable for capturing the psychological effects assumed by the prospect model, and is easier to implement.” They suggest this point also in their conclusion on p. 39. Data analysis is hard to interpret because of the many assumptions made. Conclusions on p. 39 are nice - properties of value function and probability transf. in OPT hold - Parametric fitting of utility (“that smooth out errors”) provide predictions superior to those of directly assessed values. - SG doesn’t do well: certainty method (direct assessment of values without risk present) is easier to implement, and more accurate, than gamble method (where risky choices are used). (This suggests strongly that they are willing to interpret the value function in prospect theory as riskless.) - probability transf. seems to be different for gains than for losses - concave utility for gains, convex utility for losses: p. 30, where two-piece u is concave for gains, convex for losses (They use term value function here. The term utility function refers to what utility would be if expected utility were to hold, so is less interesting I think.) P. 39: “It is not uncommon in consumer research that tradeoffs are made between generality of a model estimated and burden on respondents.” real incentives: use hypothetical choices. P. 39: argue for parametric fitting as opposed to parameter-free methods: “analytical forms for the utility and value functions that smooth out errors provide superior predictions than directly assessed values.” %} Currim, Imran S. & Rakesh K. Sarin (1989) “Prospect versus Utility,” Management Science 35, 22–41. {% Seems to write the following, which means that he suggested transforming probabilities (!), on p. 284/285 (citation from Keynes 1921, p. 314, translation into English given after): “Il me sembloit [in reading Bernoulli’s Ars Conjectandi] que cette matière avoit besoin d’être traitée d’une manière plus claire; je voyois bien que l’espérance étoit plus grande, 10 que la somme espérée étoit plus grande, 20 que la probabilité de gagner l’étoit aussi. Mais je ne voyois pas avec la même évidence, et je ne le vois pas encore, 10 que la probabilité soit estimée exactement par les méthodes utisées; 20 que quand elle le seroit, l’espérance doive être proportionnelle à cette probabilité simple, plutôt qu’à une puissance ou même à une fonction de cette probabilité; 30 que quand il y a plusieurs combinaisons qui donnent différens avantages ou différens risques (qu’on regarde comme des avantages négatifs) il faille se contenter d’ajouter simplement ensemble toutes les espérances pour avoir l’espérance totale.” [italics from the original] My translation into English is: “It seemed to me [in reading Bernoulli’s Ars Conjectandi] that this material needs to be treated more clearly; I saw well that the expectation is larger, 10 that the expected sum is larger, 20 that the probability of winning is so too. But I did not see the same evidence, and I still do not see, 10 that the probability were estimated exactly by the methods used; 20 that if it were, the expectation should be proportional to that simple probability, rather than to a power or even to a function of that probability; 30 that if there are several combinations that give different averages or different risks (which one considers as negative averages) one had to be satisfied to simply add together all these expectations for having the total expectation.” [italics from the original] %} D’Alembert (1768) “Opuscules Mathématiques, vol. iv., (extraits de lettres).” {% Principle of Complete Ignorance %} d’Amador, Risueňo (1837) “Le Calcul des Probabilités Appliqué à la Médicine,” Bulletin de l’Académie Royale de Medicine 1, 622–679. {% Principle of Complete Ignorance; P. 33: “Votre principe vous interdit cette recherche des applications individuelles: car le problème de numéristes n’est pas de guérir tel ou tel malade, mais d’en guérir le plus possible sur un totale déterminé. Or ce problème est essentiellement anti-médicale.” My translation: “Your problem prohibits you that investigation of individual applications: because the problem of the numerists is not to cure this or that ill person, but to cure the largest possible on a determined total. Hence, this problem essentially is anti-medical.” %} d’Amador, Risueňo (1837) “Memoire sur Le Calcul des Probabilités Appliqué à la Médicine, Lu à l’Académie Royale de Médicine dans Sa Scéance du 25 Avril 1837.” Baillière-J.B., Librairie de l’Académie Royale de Médicine, Paris. {% %} D’Ambrosio, Bruce (1987) “Truth Maintenance with Numeric Certainty Estimates,” Proceedings of the 3rd IEEE Conference on AI Applications, Orlando, Fla., 244–249. {% %} d’Aspremont, Claude & Louis-André Gérard-Varet (1979) “Incentives and Incomplete Information,” Journal of Public Economics 11, 25–45. {% Characterize Savage’s (1954) SEU but for a finite state space and continuous utility, using different axioms than did Wakker (1984) or Gul (1992) who varied upon Savage the same way. They assume two equally likely states of nature, so that they can compare utility differences. I write ~* if the pairs have the same utility difference, measured this way (Wakker 1984 used a tradeoff tool to get the same). Their main axiom is Difference-Scale Neutrality (p. 72), which requires that f g iff h k if there is a state of nature t such that, for all states s, f(t)f(s) ~* h(t)h(s) and (f(t)g(s) ~* h(t)k(s). That is, all utilities of h and k are like those of f,g, only moved up by U(h(t)) U(f(t)). Then utility differences are the same for each state in both decisions, so that the condition is necessary for SEU. They assume this axiom and separability (sure-thing principle). %} d’Aspremont, Claude & Louis Gevers (1990) “Invariance, Neutrality and Weakly Continuous Expected Utility.” In Jean-Jaskold Gabszewicz, Jean-François Richard, & Laurence A. Wolsey (eds.) Economic Decision-Making: Games, Econometrics and Optimisation: Contributions in honour of Jacques H. Drèze, 87–100, North-Holland, Amsterdam. {% discounting normative: Rothbard (1990) writes that he “inaugurated the tradition of moralistically deploring time preference as an over-estimation of a present that can be grasped immediately by the senses,” referring to Kauder (1965) for it. %} da Volterra, Gian Francesco Lottini (1574) “Avvedimenti Civili.” {% error theory for risky choice; gives axiomatization of probabilistic version of EU. Can account for Allais paradoxes. %} Dagsvik, John K. (2008) “Axiomatization of Stochastic Models for Choice under Uncertainty,” Mathematical Social Sciences 55, 341–370. {% Z&Z %} Dahlby, Bey G. (1981) “Adverse Selection and Pareto Improvements through Compulsory Insurance,” Public Choice 37, 548–558. {% %} Dahlby, Bey G. (1987) “Inequality Measures in a Harsanyi Framework,” Theory and Decision 22, 187–202. {% %} Dai, Zhixin, Robin M. Hogarth, & Marie Claire Villeval (2015) “Ambiguity on Audits and Cooperation in a Public Goods Game,” European Economic Review 74, 146–162. {% Finds, according to Karmarkar, overestimation of lower probabilities and underestimation of higher %} Dale, H.C.A. (1959) “A Priori Probabilities in Gambling,” Nature 183, 842–843. {% %} Dalkey, Norman C. (1949) “A Numerical Scale for Partially Ordered Utilities,” Rand memo 296, Dec. 5. {% %} Dalkey, Norman C. (1953) “Equivalence of Information Patterns and Essentially Determinate Games.” In Harold W. Kuhn & Albert W. Tucker (eds.) Contributions to the Theory of Games II, 217–243, Princeton University Press, Princeton NJ. {% %} Dalton, Patricio S. & Sayantan Ghosal (2012) “Decisions with Endogenous Frames,”.Social Choice and Welfare 38, 585–600. {% Assumes that an agent is given the info that the true probability belongs to some set of probability measures, and no other info. So, much like multiple priors, although the author does not refer to that. Assumes that the agent does EU, and formulates and discusses some axioms for updating. There are not many literature references. %} Damiano, Ettore (2006) “Choice under Limited Uncertainty,” Advances in Theoretical Economics 6, issue 1, article 5. {% Tries to study emotions at a low, material, level of aggregation, opening his lecture with: “Emotions are chemical and neural responses, forming a pattern” (own little expertise = meaning of life) Damasio, Antonio (2001) June 15, lecture at Amsterdam. {% probability communication: not only numerical but also graphical. For the latter they use pie charts and icon arrays. The pie charts don’t perform well, agreeing with previous findings in the literature, and even enhance risk aversion. Other than that, graphs reduce (but do not eliminate) risk aversion, which can be taken as a move in a rational direction. %} Dambacher, Michael,Peter Haffke, Daniel Groß, & Ronald Hübner (2016) “Graphs versus Numbers: How Information Format Affects Risk Aversion in Gambling,” Judgment and Decision Making 11, 223–242. {% %} Dana, Rose-Anna (2005) “A Representation Result for Concave Schur Concave Functions,” Mathematical Finance 15, 615–634. {% preference for flexibility %} Danan, Eric (2003) “A Behavioral Model of Individual Welfare,” EUREQua, Université de Paris I. {% preference for flexibility %} Danan, Eric (2003) “Revealed Cognitive Preference Theory,” EUREQua, Université de Paris I. {% preference for flexibility %} Danan, Eric (2005) “Money Pumps for Incomplete and Discontinuous Preferences,” Dept. d'Economia i Empresa, Universitat Pompeu Fabra, Barcelona, Spain. {% preference for flexibility %} Danan, Eric (2008) “Revealed Preference and Indifferent Selection,” Mathematical Social Sciences 55, 24–37. {% This paper considers situations of incomplete preference, some consistency principles, and possibly random selection in case of no preference. It shows that random selection under absence of preference can nevertheless lead to inconsistencies (that under some assumptions can be led into money pumps). In a way, there is no space for incompleteness, and one still better satisfy the consistency conditions throughout also if perceived nonpreference, and one should not just do random choice. %} Danan, Eric (2008) “Randomization vs. Selection: How to Choose in the Absence of Preference?,” Management Science 56, 503–518. {% Preference aggregation for multiple priors references. Unambiguous Pareto optimality says that the social preference should respect unanimously agreed individual preferences if those all are unambiguous (hold for every prior). If there is enough overlap between the individuals (something like the intersections of their prior sets not being empty) then a social preference relation, multiple priors type, exists. Social utility then is an affine aggregate of the individual utilities. %} Danan, Eric, Thibault Gajdos, Brian Hill, & Jean-Marc Tallon (2016) “Robust Social Decisions,” American Economic Review 106, 2407–2425. {% Harsanyi’s aggregation, but with incomplete preferences through multi-utility functions and unanimity criterion. %} Danan, Eric, Thibault Gajdos, & Jean-Marc Tallon (2013) “Aggregating Sets of von Neumann-Morgenstern Utilities,” Journal of Economic Theory 19, 663–668. {% DOI: http://dx.doi.org/10.1257/mic.20130117 Extend Harsanyi’s beautiful aggregation theorem to incomplete preferences, with sets of utility functions and unanimous agreement. I did not study enough to see the relation with their 2013 JME paper. %} Danan, Eric, Thibault Gajdos, & Jean-Marc Tallon (2015) “Harsanyi’s Aggregation Theorem with Incomplete Preferences,” American Economic Journal: Microeconomics 7, 61–69. {% Theoretical study on preferences over menus. %} Danan, Eric, Ani Guerdjikova, & Alexander Zimper (2012) “Indecisiveness Aversion and Preference for Commitment,” Theory and Decision 72, 1–13. {% preference for flexibility %} Danan, Eric & Anthony Ziegelmeyer (2004) “Are Preferences Incomplete? An Experimental Study Using Flexibe Choices,” EUREQua, Université de Paris I. {% foundations of quantum mechanics, some nice references to people (a.o., Piron) who say that probability distribution over place/momentum does not exclude that these things be called properties. Paper itself does not seem to contribute to that question other than linguistically %} Daniel, Wojciech (1989) “Bohr, Einstein and Realism,” Dialectica 43, 249–261. {% revealed preference %} Danilov, Vladimir I. & Gleb A. Koshevoy (2006) “A New Characterization of the Path Independent Choice Functions,” Mathematical Social Sciences 51, 238–245. {% Z&Z %} Danzon, Patricia (2002) “Welfare Effects of Supplementary Insurance: A Comment,” Journal of Health Economics 21, 923–926. {% %} Darjinoff, Karine (1998) “An Experimental Study of Insurance Behavior,” LAMIA, Paris. {% %} Darjinoff, Karine (1999) “Experimental Tests of Private Valuations and Binary Choices in Insurance Decisions,” LAMIA, Paris. {% ratio-difference principle %} Darke, Peter R. & Jonathan L. Freedman (1993) “Deciding whether to Seek a Bargain: Effects of Both Amount and Percentage off,” Journal of Applied Psychology 78, 960–965. {% gender differences in risk attitudes: with simple certainty equivalents (BDM: Becker-DeGroot-Marschak), women were not more risk averse than men. In 2nd part of experiment, subjects had to make risky decisions for others than themselves. The predicted risk attitudes of others was mix of own risk attitude and risk neutrality, and subjects believed (incorrectly in this group) that women would be more risk averse. %} Daruvala, Dinky (2007) “Gender, Risk and Stereotypes,” Journal of Risk and Uncertainty 35, 265–283. {% A fancy statistical technique is developed and applied to returns to stock markets in five countries, to find that the index of relative risk aversion is not constant over time. %} Das, Samarjit & Nityananda Sarkar (2010) “Is the Relative Risk Aversion Parameter Constant over Time? A Multi-Country Study,” Empirical Economics 38, 605–617. {% decreasing/increasing impatience: provides theoretical arguments for the possibility of increasing impatience. Consider intertemporal choice when there is probability of earlier or later payment than thought. Show that all kinds of plausible probability distributions of the latter can imply decreasing (as in hyperbolic) discounting at t=0. There are also plausible probability distributions that imply increasing discounting at t=0, such as the example of Sozou on p. 1292, and the example at the beginning of §III, pp. 1294-1295. In these examples of nonconstant discounting, a reversal of preference at a different time point is not dynamic inconsistency, but can simply follow from Bayesian updating: arriving at the later time point without consumption received yet gives the extra information that the “risk” of receiving the consumption before that later time point did not happen. P. 1290, footnote 2, nicely explains how the term hyperbolic discounting originally meant something specific (discount rate depending inversely on time) but nowadays is used for anything with decreasing discounting. P. 1291, first para of §I, mentions that discounting can be due to uncertainty about the future, referring to Yaari (1965) for it. DC = stationarity: dynamic consistency; End of §I carefully distinguishes between variation in the time of consumption (“comparisons across decision problems”) versus variation in the time of decision making (“comparison within the same decision problem”) and properly says that the former is not a violation of dynamic consistency. §IV gives example of preference change when decision time point changes, so dynamic inconsistency, which however rationally follows because the model is more complex than just single intertemporal choice and more is going on. The more going on is that it is in fact a repeated decision with learning, where learning is taken in an evolutionary sense. Refer for it to experimental evidence with pigeons. %} Dasgupta, Partha & Eric Maskin (2005) “Uncertainty and Hyperbolic Discounting,” American Economic Review 95, 1290–1299. {% Seems to describe the early Buffon who argued that all probabilities < .0001 be treated as “morally” equal to zero. %} Daston, Lorraine J. (1980) “Probabilistic Expectation and Rationality in Classical Probability Theory,” Historia Mathematica 7, 234–260. {% foundations of probability 1837-1842 six authors discussed objective-subjective probabilities. Originally, probabilities were parimarily taken as subjective/epistemic, although (observed) relative frequencies were also considered from the beginning. Around 1840 the objective concept became more established. Cournot, well-known for his equilibrium, was important here. The first part of the paper, pp. 332 ff., discussed the terms objective versus subjective, which also developed and changed over time. Quite some authors argued that only certainty can be objective (p. 332 middle). It surely can achieve a high degree of objectivity, not available to uncertainty. P. 336 middle discusses separation of inside and outside of human mind (Descartes) P. 335 l. -5/-4 cites Poisson on arguing that the law of large numbers is the “base of all applications of the calculus of probabilities,” which is close to the frequentist interpretation. The next text cites Poisson on using the term probability for subjective probabilities (which he, thus, still did consider) and the term chance for objective probabilities. The chance of heads-tails is not precisely 0.5, but the probability is. During my collaborations with Amos Tversky, early 1990s, I noticed that Amos liked to use the term chance for objective probabilities. P. 336 middle cites Cournot (1843): The "subjective probabilities" based on equal ignorance of outcomes were fit only for the "frivolous use of regulating the conditions of a bet" [9, 111, 288], and were moreover the "cause of a crowd of equivocations [which] have falsified the idea one ought to have of the theory of chances and of mathematical probabilities" [9, 59]. [italics added here] Then it cites Cournot on calling upon statisticians to avoid subjective inputs. P. 337 cites later editions of Mill (1843) on admitting the (subjective) more probable than concept and relating it to betting on!: Mill grudgingly conceded that "as a question of prudence" we might rationally assume that "one supposition is more probable to us than another supposition," and even bet on that assumption "if we have any interest at stake" and if we were in the desperate (and rare) situation of having no relevant experience whatsoever [31, 7:535-536]. P. 337 middle: Mill curiously remained the most traditional of the revisionists in his interpretation of all probabilities as epistemic. My opinion may fit with Mill: basically, all probabilities are subjective, but in communications and virtually all applications except the final decision almost exclusively the objective probabilities are relevant. P. 339 starts with an interesting topic: “The objectivity of chance in a deterministic world.” It discusses stable vs. variable causes, but most I could not understand. %} Daston, Lorraine J. (1994) “How Probabilities Came to Be Objective and Subjective,” Historia Mathematica 21, 330–344. {% value of information: signal dependence designates situations in which new info affects not only beliefs but also the utility of outcomes. Shows that value of experimentation will always be positive if cross-derivative of the value function with respect to beliefs and the signal is positive. Otherwise, value of info may be negative. information aversion: p. 579 nicely describes my 1988 information-aversion paper: “First, if an agent violates the independence axiom of expected utility, then the agent may be dynamically inconsistent and accordingly may prefer less information to more.” %} Datta, Manjira, Leonard J. Mirman, & Edward E. Schlee (2002) “Optimal Experimentation in Signal-Dependent Problems,” International Economic Review 43, 577–607. {% Seem to find that percentage of lawyers negatively affects the GNP growth rate. Seem to write: “since lawyers are by and large among the most intelligent members of society, their diversion from normal and especially from growth-enhancing economic activities, has the effect of reducing both the level of aggregate output and its rate of growth.” %} Datta, Samar K. & Jeffrey B. Nugent (1986) “Adversary Activities and Per Capita Income Growth, World Development 14, 1457–1461. {% violation of objective probability = one source: CRRA risk aversion measures were elicited from 900 subjects in two ways: first, using choice lists, second, choosing one from 6 prospects (considered simpler). The simpler task works better for non-sophisticated subjects, and the more complex task works better for sophisticated subjects. Consider gender differences. %} Dave, Chetan, Catherine C. Eckel, Cathleen A. Johnson & Christian Roja (2010) “Eliciting Risk Preferences: When Is Simple Better?,” Journal of Risk and Uncertainty 41, 219–243. {% %} David, Herbert A. (1988) “The Method of Paired Comparisons.” Griffin, London, 1988; 2nd edn. {% ambiguity seeking for losses %} Davidovich, Liema & Yossi Yassour (2009) “Ambiguity Preference,” School of Social Sciences and Management, Ruppin Academic Center, Emek Hefer 40250, Israel. {% %} Davidson, Donald (1974) “The Philosophy of Mind.” In Jonathan Glover (ed.) The Philosophy of Mind, 101–110, Oxford University Press, New York. {% free-will/determinism: seems to find free will/behavior and determinism irreconcilable %} Davidson, Donald (1990) “The Structure and Content of Truth,” Journal of Philosophy 87, 279–328. {% First ? with money pump argument; ascribe idea to Norman Dalkey; vNM-utility=strength.pr.??; Probabilities nonadditive!!! %} Davidson, Donald, John C.C. McKinsey, & Patrick Suppes (1955) “Outlines of a Formal Theory of Value, I,” Philosophy of Science 22, 140–160. {% strength-of-preference representation; seem to have introduced the crossover property; just noticeable difference: seem to suggest that those can be useful for risky decision theory. Nicely puts forward that probabilistic decision theory can serve as a basis for strength of preference and cardinal utility. %} Davidson, Donald & Jacob Marschak (1956) “Experimental Tests of Stochastic Decision Theory.” In C. West Churchman & Philburn Ratoosh (eds.) Measurement: Definitions and Theories, Wiley, New York. {% risky utility u = strength of preference v (or other riskless cardinal utility, often called value); footnote 5 gives nice discussion that vNM bring in independence by taking indifference as congruence. Utility of gambling: p. 266 P. 266 discusses that indifference cannot easily be observed from revealed preference. %} Davidson, Donald & Patrick Suppes (1956) “A Finitistic Axiomatization of Utility and Subjective Probability,” Econometrica 24, 264–275. {% Try to improve Mosteller & Nogee (1951), for one thing by avoiding the certainty effect by not using certain outcomes. So, utility elicitation; risky utility u = strength of preference v (or other riskless cardinal utility, often called value); vNM utility is as well curved for small amounts, as for large (got this from Lopes, 1984) They seem to investigate the “probabilistic reduction” principle by which I mean the basic assumption of decision under risk, meaning that for an act only the probability distribution generated over the outcomes matters. Real incentives: did it with repeated payments (so income effect). Implementing losses: losses from prior endowment mechanism: that, however, might not suffice to always keep the balance positive. If their balance became negative, they stopped the experiment and for the rest of the time had to work in the laboratory. Income effect, and attempt to moderate it, are described on p. 183-184. P. 198: “Perhaps not very surprisingly, most subjects were somewhat sanguine about small wins and conservative with respect to small losses.” That is, they find risk aversion and concave utility for losses and risk seeking/convexity for gains. %} Davidson, Donald, Patrick Suppes, & Sidney Siegel (1957) “Decision Making: An Experimental Approach.” Stanford University Press, Stanford, CA; Ch. 2 has been reprinted in Ward Edwards & Amos Tversky (1967, eds.) Decision Making, 170–207, Penguin, Harmondsworth. {% foundations of probability, Knight risk-uncertainty %} Davidson, Paul (1991) “Is Probability Theory Relevant for Uncertainty? A Post Keynesian Perspective,” Journal of Economic Perspectives 5 no. 1, 129–143. {% foundations of quantum mechanics %} Davies, E. Brian (2005) “Some Remarks on the Foundations of Quantum Mechanics,” British Journal for the Philosophy of Science 56, 521–539. {% %} Davies, Greg B. (2006) “Rethinking Risk Attitude: Aspiration as Pure Risk,” Theory and Decision 61, 159–190. {% PT is fit to equity returns data from the US and the UK. They confirm the findings of Tversky & Kahneman (1992): concave utility for gains, convex utility for losses: find concave utility for gains, convex utility for losses, closer to linear for losses than for gains, inverse-S probability weighting, and a loss aversion between 2 and 3. Remark 5 of version of September 24, 2003: the optimal equity allocation is highly sensitive to loss aversion. %} Davies, Greg B. & Stephen E. Satchell (2003) “Continuous Cumulative Prospect Theory and Individual Asset Allocation,” University of Cambridge, UK. {% %} Davies, Greg B. & Stephen E. Satchell (2006) “The Behavioural Components of Risk Aversion,” Journal of Mathematical Psychology 51, 1–13. {% Seem to have something similar to the smooth model. %} Davis, Donald B. & M.-Elisabeth Paté-Cornell (1994) “A Challenge to the Compound Lottery Axiom: A Two-Stage Normative Structure and Comparison to Other Theories.” Theory and Decision 37, 267–309. {% Ch. 8 seems to discuss paying in probabilities of a prize rather than in $, so as to get linearity of utility, and to find that empirical evidence on it is mixed at best. random incentive system: p. 455: the authors criticize the random incentive system as justified by Starmer & Sugden (1991) by arguing that with only a 0.5 probability of a choice played for real, the expectations are 0.5 smaller and that it would accordingly be better to multiply all outcomes by 2. I think that this criticism is irrelevant because it crucially assumes expected value. Their suggestion is even harmful under the plausible assumption of isolation. The point is tested by Laury (2005, working paper) who finds that it does not arise. %} Davis, Douglas D. & Charles A. Holt (1993) “Experimental Economics.” Princeton University Press, Princeton NJ. {% Developed MYCIN, using certainty factors with ad hoc rules to combine them. Mention need for a normative theory. %} Davis, Randall, Bruce G. Buchanan, & Edward H. Shortliffe (1977) “Production Rules as a Representation for a Knowledge-Based Consultation System,” Artificial Intelligence 8, 15–45. {% measure of similarity %} Davison, Mark L. (1992) “Multidimensional Scaling.” Krieger Publishing, Malabar, Fl. {% %} Dawes, Robyn M. (1990) “False Consensus Effect,” Insights in Decision Making: A Tribute to Hillel J. Einhorn, University of Chicago Press, Illinois, 179–199. {% verbal textbook %} Dawes, Robyn M. (1988) “Rational Choice in an Uncertain World.” Harcourt Brace Jovanovich, San Diego. {% intuitive versus analytical decisions; Argue that statistical reasoning is superior to intuitive reasoning. All examples and references exclusively concern clinical prediction. Kleinmuntz et al. (1990, Science) will criticize the paper for being too narrow. %} Dawes, Robyn M., David Faust, & Paul E. Meehl (1989) “Clinical versus Actuarial Judgment,” Science 243, 1668–1673. {% suspicion under ambiguity: incomplete preliminary research ideas, but interesting. Unfortunately, a paper was never completed. %} Dawes, Robyn M., Gunne Grankvist, & Jonathan W. Leland (1999) “Avoiding the “Ellsberg Bag” as Avoiding a “Stacked Deck” Possibility, rather than Avoiding Ambiguity,” Carnegie Mellon University. {% Introduced calibration %} Dawid, A. Philip (1982) “The Well Calibrated Bayesian,” Journal of the American Statistical Association 77, 605–613. {% foundations of statistics, Fisher versus others %} Dawid, A. Philip (1991) “Fisherian Inference in Likelihood and Frequential Frames of Reference” and discussion, Journal of the Royal Statistical Society, Ser. B, 53, 79–109. {% proper scoring rules; A duality between decisions and outcomes is exploited. %} Dawid, A. Philip (2007) “The Geometry of Proper Scoring Rules,” Annals of the Institute of Statistical Mathematics 59, 77–93. {% foundations of probability; briefly lists many interpretations. Focuses on whether probability refers to individuals or to grous. %} Dawid, A. Philip (2007) “On Individual Risk,” Synthese 194, 3445–3474. {% DOI: 10.1214/12-AOS972 proper scoring rules: extend locality to also allow dependence on the scores in a neighborhood of the observed event. Then more than just the logarithmic function can do it. %} Dawid, A. Philip, Steffen Lauritzen, & Matthew Parry (2012) “Proper Local Scoring Rules on Discrete Sample Spaces,” Annals of Statistics 40, 593–608. {% verbal textbook %} Dawid, A. Philip & Mervyn Stone (1982) “The Functional-Model Basis of Fiducial Inference” (plus discussion), Annals of Statistics 10, 1054–1074. {% Discusses levels of selection including that of the group, the individual, and the gene itself. Seems that he introduced the concept of a meme. %} Dawkins, Richard (1976) “The Selfish Gene.” Oxford University Press, Oxford. {% Explains Gould’s theory. Gould invented theory of stepwise evolution %} Dawkins, Richard (1985) “The Blind Watchmaker.” Oxford University Press, Oxford. {% conservation of influence. Discusses Tinbergen’s (1963) four questions, and adds four questions: who benefits from action such as singing of bird. Are they genes, individual of bird, bird-species, gene pool?; %} Dawkins, Richard (2004) “Lecture of May 19’04 in St. Pieterskerk in Leiden, the Netherlands.” {% %} Day, Brett & Graham Loomes (2010) “Conflicting Violations of Transitivity and where They May Lead Us,” Theory and Decision 68, 233–242. {% What the title says, with many statistics on nrs. of publications. %} de Almeida, Adiel Teixeira, Marcelo Hazin Alencar, Thalles Vitelli Garcez, & Rodrigo José Pires Ferreira (2017) “A Systematic Literature Review of Multicriteria and Multi-Objective Models Applied in Risk Management,” IMA Journal of Management Mathematics 28, 153–184. {% Use prospect theory to analyze the risk perception of traffic participants. Use Tradeoff method to measure utility for losses.. Find that it is predominantly convex (concave utility for gains, convex utility for losses). %} de Blaeij, Arianne T. & Daniel J. van Vuuren (2003) “Risk Perception of Traffic Participants,” Accident Analysis and Prevention 35, 167–175. {% %} De Bock G.H., S.A. Reijneveld, Jan C. van Houwelingen, André Knottnerus, & Job Kievit (1999) “Multi-Attribute Utility Scores: Can They Be Used to Predict Family Physicians’ Decisions Regarding Patients Suspected from Sinusitis,” Medical Decision Making 19, 58–65. {% If endowments are unambiguous, then ambiguity aversion reduces trade for a very general class of preference models. %} de Castro, Luciano I. & Alain Chateauneuf (2011) “Ambiguity Aversion and Trade,” Economic Theory 48, 243–273. {% In an economy, efficiency and incentive compatibility is satisfied iff agents have maxmin individual preferences. (Complete maxmin, not maxmin multiple priors.) %} de Castro, Luciano & Nicholas C. Yannelis (2011) “Uncertainty, Efficiency and Incentive Compatibility,” working paper. {% Introduces behavioral agents into implementation problems. %} de Clippel, Geoffroy (2014) “Behavioral Implementation,” American Economic Review 104, 2975–3002. {% %} de Clippel, Geoffroy, Hans J.M. Peters, & Horst Zank (2004) “Axiomatizing the Harsanyi Solution, the Symmetric Egalitarian Solution and the Consistent Solution for NTU-Games,” International Journal of Game Theory 33, 145–158. {% The following poem, translated from Dutch, nicely illustrates loss aversion. By reframing the status quo, a loss is turned into a gain in the last four lines. The fool in the beginning of the poem is also trying to get mileage from playing with the reference point. Translation (joint with Thom Bezembinder; the Dutch word “geluk” means both happiness and lucky thing. This identity is lost in the translation. ) “Lucky thing, it could have been worse” As for the fool from the joke,/
Maybe this is happiness: lucky thing, it could have been worse/ maybe happiness is: lucky thing/ That I can remember you, for instance,/ instead of someone else./ Original text: “Nog een geluk dat” Zoals met de gek uit het grapje/ die zich voortdurend met een hamer/ op het hoofd sloeg, en naar de reden gevraagd, zei/ “Omdat het zo prettig is als ik ermee ophou” -/ zo is het een beetje met mij. Ik ben ermee opgehouden/ je te verliezen. Ik ben je kwijt./ Misschien is dat geluk: een geluk bij een ongeluk./ Misschien is geluk: nog een geluk dat./ Dat ik aan jou kan terugdenken, bv.,/ in plaats van aan een ander./ %} de Coninck, Herman (2002) “Nog een Geluk Dat.” In the book De Gedichten, Arbeiderspers, Amsterdam, 10th edn., p. 136. {% Abstract, where fuzzy measure is what is also called Sugeno integral: “…in a numerical context, the Choquet integral is better suited than the fuzzy integral for producing coherent upper previsions starting from possibility measures.” %} De Cooman, Gert (2000) “Integration in Possibility Theory.” In Michel Grabisch, Toshiaki Murofushi & Michio Sugeno (eds.) Fuzzy Measures and Integrals: Theory and Applications, 124–160, Physica-Verlag, Berlin. {% Dutch book %} de Finetti, Bruno (1930) “Problemi Determini e Indetermini nel Calcolo delle Probabilità,” Rendiconti della Academia Nazionale dei Lincei XII, 367–373. {% §13, Postulate 4 introduces additivity axiom for qualitative probability. Dutch book. Download 7.23 Mb. Share with your friends:
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