Bibliography
Download 7.23 Mb.
|
- Navigate this page:
- C = stationarity
- PT falsified; probability weighting depends on outcomes
| §4.3, p. 254 last para, erroneously claims that Epstein wanted unambiguous to be exogenous. Epstein was very strong on it having to be endogenous (with me disagreeing much).
§§4.5.1-4.5.3 discuss concepts of ambiguity and ambiguity aversion but cannot give a clear picture because they fall victim to what I called an historical accident in my book Wakker (2010, §11.6): as in Schmeidler 1989, they take EU as given, and equate convex (pessimistic) weighting function with ambiguity aversion and Ellsberg. I think that convexity of the weighting function, an absolute property, is better related to the Allais paradox. The Ellsberg paradox and ambiguity aversion concern a relative concept: more pessimism/convexity for uncertainty than for risk. Because they take EU as given, being more convex for uncertainty than for risk happens to coincide with being convex, and the relative and absolute concepts get confused. P. 259, on rectangularity of Epstein & Schneider (2003): Sarin & Wakker (1998, JRU, pp. 87–119), Theorem 2.1 had it before, calling it the reduced family. %} Etner, Johanna, Meglena Jeleva, & Jean-Marc Tallon (2012) “Decision Theory under Ambiguity,” Journal of Economic Surveys 26, 234–270. {% %} European Commission (2002) “Eurobarometer—Public Opinion in the European Union.” Report no. 57, Spring 2002. {% On use of behavioral economics in Europe. %} European Commission (2013) “Applying Behavioural Sciences to EU Policy-Making (2013), {% I dislike expressions of nationalism in research. Accordingly, I think that it was a big marketing mistake calling this measure “EUROQOL.” %} EuroQol Group (1990) “EuroQol: A New Facility for the Measurement of Health-Related Quality of Life,” Health Policy 16, 199–208. {% Ismail Mehmet pointed out to me in 2017 that may be this paper, rather than Zermelo (2013), was the first to use backward induction to prove that chess is determined. The author later became world champion chess (1935-1937). So, he applied his theorem skillfully. %} Euwe, Max (1929) “Mengentheoretische Betrachtungen Über das Schachspiel,” Koninklijke Akademie van Wetenschappen, Proceedings XXXII, no 5, 633–642. {% %} Evans, Anthony M. & Joachim I. Krueger (2017) “Ambiguity and Expectation-Neglect in Dilemmas of Interpersonal Trust,” Judgment and Decision Making 12, 584–595. {% %} Evans, Chris D.H., John Hughes, & Julia Houston (2002) “Significance-Testing the Validity of Ideographic Methods: A Little Derangement Goes a Long Way,” British Journal of Mathematical and Statistical Psychology 55, 385–390. {% Paper elicits certainty equivalents of gambles through BDM (Becker-DeGroot-Marschak) in individual choice. Also elicits prices people pay for buying gambles, through fifth-price sealed-bid auctions, which should reveal true willingness to pay. The latter is called market level. At the market level, there are fewer violations of betweenness than at the individual level. The author points out that the analysis suggests that the phenomenon is due to statistical effects, not due to differences in the individual behavior. It may be the center-of-distribution-orientedness of the market procedure rather than true betweenness that does it. Another point is that the choices are repeated. The participants receive prior endowment and pay/win sequentially in several gambles. Any theory, prospect theory, betweenness, EU, etc., recommends expected value maximization in often-repeated-choice-with-the-sum-of-gains-to-be-maximized, because of the law of large numbers. Participants may be doing something between that and isolated evaluations. %} Evans, Dorla A. (1997) “The Role of Markets in Reducing Expected Utility Violations,” Journal of Political Economy 105, 622–636. {% %} Evans, Dylan (2012) “Risk Intelligence: How to Live with Uncertainty.” London: Atlantic Books. {% %} Evans, Michael J. (2013) “What Does the Proof of Birnbaum’s Theorem Prove?,” working paper. {% foundations of statistics; presents measure-theoretic tools to extend results of their 86 paper to infinite case %} Evans, Michael J., Donald A.S. Fraser, & George Monette (1985) “On Regularity for Statistical Models,” Canadian Journal of Statistics 13, 137–144. {% A later paper is Gandenberger (2015). foundations of statistics; Proves a beautiful result. It proves (for discrete sample space) that the likelihood principle follows from conditionality principle alone, without needing sufficiency postulate. It, therefore, reinforces Alan Birnbaum’s famous result. The trick is not to condition on two different values of an ancillary statistic as does Birnbaum’s proof, but instead on values of two different ancillary statistics. After presenting its beautiful result reinforcing the force of the likelihood principle, the paper in fact argues against the likelihood principle. I do not understand the criticism. For instance, if the llh. principle says that models M, M', and M'' are equivalent the authors argue that the llh. principle says that different models are appropriate and that therefore the llh. principle gives contradictory recommendations. Am I missing something? %} Evans, Michael J., Donald A.S. Fraser, & George Monette (1986) “On Principles and Arguments to Likelihood” (with discussion) Canadian Journal of Statistics 14, 181–199. {% %} Even, Shimon (1979) “Graph Algorithms.” Pitman, London. {% Generalizates Choquet integral. Not only top-down or bottom-up, but other arrangements are considered. The concave integral is the infimum over all arrangements. %} Even, Yaarit & Ehud Lehrer (2014) “Decomposition-Integral: Unifying Choquet and the Concave Integrals,” Economic Theory 56, 33–58. {% Examines the vNM EU axioms without completeness. %} Evren, Özgür (2008) “On the Existence of Expected Multi-Utility Representations,” Economic Theory 35, 575–592. {% Modifies Dubra, Maccheroni, & Ok (2004) by also maintaining strict preference. Applications to game theory. %} Evren, Özgür (2014) “Scalarization Methods and Expected Multi-Utility Representations,” Journal of Economic Theory 151, 30–63. {% probability communication: present probabilities numerically, using icon arrays (matrices with little bars, and part of bars highighted), and using spinners. Numerical probabilities fare worse. Several studies have shown that people are bad at estimating angles so that pie charts and spinners will not be so good. %} Eyler, Rachel F., Sara Cordes, & Benjamin R. Szymanski (2017) “Utilization of Continuous “Spinners” to Communicate Risk,” Medical Decision Making 37, 725–729. {% ”known composition mapping result” with quasi-concave iso concave functions %} Fabella, Raul V. (1992) “Quasi-Concave (Composition) Functions with Nonconcave Argument Functions,” International Economic Review 33, 473–477. {% Show that every inner measure is a belief function. A belief function can be mapped isomorphically into another space where it is an inner measure. %} Fagin, Ronald & Joseph Y. Halpern (1991) “Uncertainty, Belief, and Probability,” Computational Intelligence 7, 160–173. {% three-prisoners problem; Propose a way to update belief functions, and prove in Theorem 3.5 that this method, unlike the Dempster/Shafer method, will again yield a belief function. The result was obtained independently by Jaffray (1992), who added the more complicated other direction of implication. %} Fagin, Ronald & Joseph Y. Halpern (1991) “A New Approach to Updating Beliefs.” In Piero P. Bonissone, Max Henrion, Laveen N. Kanal, & John F. Lemmer (eds.) Uncertainty in Artificial Intelligence 6, 347–374, Elsevier, Amsterdam. {% Criticizes Arkes (1991), who confused framing and reflection. Thus, this paper properly criticizes the mistake of loss aversion: erroneously thinking it is reflection: %} Fagley, Nancy S. (1993) “A Note Concerning Reflection Effects versus Framing Effects, Psychological Bulletin 113, 451–452. {% %} Fagley, Nancy S. & Paul M. Miller (1987) “The Effects of Decision Framing on Choice of Risky versus Certain Options,” Organizational Behavior and Human Decision Processes 39, 264–277. {% Social risk attitude better predicts social behavior that pure risk attitude. %} Fairley, Kim, Alan Sanfey, Jana Vyrastekova, & Utz Weitzel (2013) “Social Risk and Ambiguity in the Trust Game,” working paper. {% Argue that risk attitude w.r.t. mechanical risk can be different than in trust game, where it involves giving up control to another human being acting by conscious choice. They measure risk attitude in a mechanical context and in a “risky trust game,” which is a trust game but with probability of deception given. The two risk attitudes are uncorrelated, and only the second predicts behavior in the standard trust game. %} Fairley, Kim, Alan Sanfey, Jana Vyrastekova, & Utz Weitzel (2016) “Trust and Risk Revisited,” Journal of Economic Psychology 57, 74–85. {% %} Falk, Armin & James J. Heckman (2009) “Lab Experiments Are a Major Source of Knowledge in the Social Sciences,” Science 326, 23 Oct., 535–538. {% crowding-out: bit like that: if employer controls employees, performance decreases because employees feel it as sign of distrust. %} Falk, Armin & Michael Kosfeld (2006) “The Hidden Cost of Control,” American Economic Review 96, 1611–1630. {% DOI: HTTP://DX.DOI.ORG/10.1126/science.1231566 Thought-provoking experiment on markets eroding moral values. Here letting mouse live means that an experimental mouse that would normally have been killed, is given a decent life (average: 2 years). It need not be desirable in the sense that other people had apparently decided that this life is not worth living, and the money it takes, but now they get forced to still do it. Treatment 1. A subject can choose individually: (a) €10, but mouse will be killed; (b) the mouse will live but no money; Treatment 2 (bilateral market). . A pair of subjects can choose: (a) They let one (one!) mouse live and get no money; (b) They agree on dividing €20 and the mouse will be killed. They can take 210 rounds of bidding. In this treatment 2, one of the two subjects is called seller and is told “the life of the mouse is entrusted to your care,” but this is only framing without any strategic implication; the life of the mouse is in fact a public good and not a consumption commodity. Treatment 3 (multilateral market). 9 subjects are called sellers and 7 are called buyers. Sellers must state a minimum prize x, meaning that they accept any division of x or more for them and 20-x for a buyer. Buyers must state a maximum prize y, meaning that they accept any division of 20-y for themselves and y for the other. Note that the difference is only a matter of framing (whether you should say z or 20-z), and not strategic. Sellers and buyers are coppled by market mechanisms. Whenever a trade is made, a mouse is killed for it. They can take 210 rounds of bidding. Because there is a lack of buyers, with the firmest two sellers left alone, buyers are in a power position and selling prices of sellers (used as index in the analysis) will be relatively low. As Figure 1 shows, the price for a mouse is highest in Treatment 3, then in Treatment 2, and lowest in Treatment 1. The authors conclude that markets erode moral values. Points for discussion: (1) Treatment 2 is in fact a bargaining problem, with mouse surviving the disagreement outcome (which need not be unfavorable). Strategic considerations and fairness play a role. Also, the bargaining distracts from the moral issue, especially if experimenter demand comes in. Here also the tradeoff is between money or HALF responsibility rather than, as in treatment 1, full responsibility. I expect that in Treatment 2 most subjects simply took the fair 10-10 division of money; this nr. is not reported in the paper. (3) In Treatment (3), market considerations similarly complicate the case, where further the strategic disadvantage of the sellers complicates. Here the responsibility for a mouse’s life is quite small because a seller can think: if I don’t sell, then another seller will and the mouse will die anyhow (p. 708 2nd column bottom). The authors have a control treatment (p. 708 top of 2nd column) with a market for a consumption good where market and individual price do not differ, but this is very different (e.g. it is zero sum) from the bargaining problem of the public good of the mouse-life. The authors have a control treatment (p. 710 1st column top) where individuals do not receive €10 for sure, but a 50-50 lottery, but this 50-50 lottery will not distract the same way as the bargaining situation. The authors have a control treatment (p. 709 3rd column middle) where an individual decides, but not only he but some nondeciding other gets €10 if the money is chosen. Here indeed it is €20 per mouse life in total, and the welfare difference between Treatments (1) and (2) is controled for, but the shared- versus single-responsibility difference between Treatments (1) and (2) is not controled for. %} Falk, Armin & Nora Szech (2013) “Morals and Markets,” Science 340, 707, 10 May 2013, 707–711. {% Extends Hölder to case of maximal and minimal elements. %} Falmagne, Jean-Claude (1971) “Bounded Versions of Hölder’s Theorem with Application to Extensive Measurement,” Journal of Mathematical Psychology 8 495–507. {% %} Falmagne, Jean-Claude (1975) “A Set of Independent Axioms for Positive Hölder Systems,” Philosophy of Science 42, 137–151. {% %} Falmagne, Jean-Claude (1976) “Random Conjoint Measurement and Loudness Summation,” Psychological Review 83, 65–79. {% restricting representations to subsets: Criticizes another paper that misuses Cauchy’s functional equation, by having it only on a finite domain where it need not imply linear representation. %} Falmagne, Jean-Claude (1981) “On a Recurrent Misuse of a Classical Functional Equation Result,” Journal of Mathematical Psychology 23, 190–193. {% error theory for risky choice: Ch. 11 about probabilistic choices %} Falmagne, Jean-Claude (1985) “Elements of Psychophysical Theory.” Oxford University Press, New York. {% %} Falmagne, Jean-Claude (2004) “Meaningfulness and Order-Invariance: Two Fundamental Principles for Scientific Laws,” Foundations of Physics 34, 1341–1384. {% Seems to have introduced the Sugeno integral already for the special case of additive measures. It seems to be known as the Ky Fan distance. This was pointed out to me by Denneberg. %} Fan, Ky (1944) “Entfernung Zweier Zufälliger Grössen und die Konvergenz nach Warscheinlichkeiten,” Mathematische Zeitschrift 49, 681–683. {% %} Fan, Ky (1956) “On Systems of Linear Inequalities.” In Harold W. Kuhn & Albert W. Tucker (eds.) Linear Inequalities and Related Systems, 99–156, Princeton University Press, Princeton, NJ. {% Show how many proper scoring rules can be derived from general functions, which is what arbitrary value function in their title refers to. They assume expected value (see end of §1.1). Fang Fang, Maxwell B. Stinchcombe, & Andrew B. Whinston (2010) “Proper Scoring Rules with Arbitrary Value Functions,” Journal of Mathematical Economics 46, 1200–1210. {% P. 1043: DC = stationarity They fit quasi-hyperbolic discounting to data on single women with children and estimate utility losses resulting from it. %} Fang, Hanming & Dan Silverman (2009) “Time-Inconsistency and Welfare Program Participation: Evidence from the Nlsy,” International Economic Review 50, 1043–1077. {% This paper introduces, on p. 1050, QALYs (without using the term), and on p. 1047 the TTO method. It precedes Torrance’s work. P. 1024 gives a nice survey on preceding ways of quantifying health outcomes. P. 1043: proposes variation of TTO, where a health state is however followed by perfect health not by death, to measure quality of life. P. 1044 proposes person tradeoff method to measure quality of life. P. 1047 proposed really Torrance’s TTO. P. 1050 formulates the QALY calculation method. %} Fanshel, Sol & James W. Bush (1970) “A Health-Status Index and Its Application to Health Servics Outcomes,” Operations Research 18, 1021–1066. {% ratio-difference principle %} Fantino, Edmund & Jay N. Goldshmidt (2000) “Differences, Not Ratios, Control Choice in an Experimental Analogue to Foraging,” Psychological Science 3, 229–233. {% Does not assume reference point known, but derives it by fitting data for each taxi driver separately. Assumes linear utility and no probability weighting. Finds that after reaching reference income level the taxi drivers indeed almost always stop. However, 2/3 don’t reach the reference income level before the shift is over and behavior is more complex. For instance, the reference level changes day by day. Thus, the author concludes that the role of reference dependence is not so clear. %} Farber, Henry S. (2008) “Reference-Dependent Preferences and Labor Supply: The Case of New York City Taxi Drivers,” American Economic Review 98, 1069–1082. {% DOI: http://dx.doi.org/10.1287/mnsc.1120.1610 . Propose nonparametric method for market consumer preference measurement. Provide arguments against parametric fitting (can have wrong family, and can either under or overfit, although I think that nonparametric fitting will only overfit more. %} Farias, Vivek F., Srikanth Jagabathula, & Devavrat Shah (2013) “A Nonparametric Approach to Modeling Choice with Limited Data,” Management Science 59, 305–322. {% PT, applications, loss aversion, politics!!! %} Farnham, Barbara (1994, ed.) “Avoiding Losses/Taking Risk; Prospect Theory and International Conflicts.” University of Michigan Press, Ann Arbor. {% %} Faro, David & Yuval Rottenstreich (2006) “Affect, Empathy, and Regressive Mispredictions of Others’ Preferences under Risk,” Management Science 52, 529–541. {% Ch. 1 introduces, Ch. 2 introduces the model later published by Chateauneuf & Faro (2009, JME), which is the multiplicative version of the variational model by Maccheroni, Massimo & Rustichini (2006). Ch. 3 provides a sign-dependent generalization, assuming ambiguity aversion (pessimism) also for losses. Ch. 4 applies it to incomplete markets. %} Faro, José H. (2005) “On the Choices under Ambiguity,” Ph.D. dissertation, Instituto Nacional de Matemática Pura e Aplicada, Rio de Janeiro. {% Considers Bewley type incomplete preferences and maxmin preferences where a homotheticity axiom implies that utility is Cobb-Douglas. %} Faro, José H. (2013) “Cobb-Douglas Preferences under Uncertainty,” Economic Theory 54, 273–285. {% Generalizes Bewley by adding the variational probability-distribution-dependent punishment term of the variational model to it. Does in in the Anscombe-Aumann framework. %} Faro, José H. (2015) “Variational Bewley Preferences,” Journal of Economic Theory 157, 699–729. {% updating: Study updating for the appealing model by Gilboa, Maccheroni, Marinacci, & Schmeidler (2010) with multiple priors and then the unanimous decisions as objectively rational, and the maxmin as subjectively rational. %} Faro, José H. & Jean Philippe Lefort (2013) “Dynamic Objective and Subjective Rationality,” {% %} Farquhar, Peter H. (1975) “A Fractional Hypercube Decomposition Theorem for Multiattribute Utility Functions,” Operations Research 23, 941–967. {% %} Farquhar, Peter H. (1977) “A Survey of Multiattribute Utility Theory and Applications.” In Martin K.Starr & Milan Zeleny (eds.) Multiple Criteria Decision Making, Vol. 6 of TIMS Studies in the Management Sciences, 59–90, North-Holland, Amsterdam. {% utility elicitation; extensive survey %} Farquhar, Peter H. (1984) “Utility Assessment Methods,” Management Science 30, 1283–1300. {% %} Farquhar, Peter H. & Peter C. Fishburn (1981) “Equivalence and Continuity in Multivalent Preference Structures,” Operations Research 29, 282–293. {% %} Farquhar, Peter H. & Peter C. Fishburn (1982) “Finite-Degree Utility Independence,” Mathematics of Operations Research 7, 348–353. {% %} Farquhar, Peter H. & Peter C. Fishburn (1983) “Indifference Spanning Analysis.” In Bernt P. Stigum & Fred Wenstop (eds.) Foundations of Utility and Risk Theory with Applications, 443–459, Reidel, Dordrecht. {% strength-of-preference representation: not representation but nice discussion %} Farquhar, Peter H. & L. Robin Keller (1989) “Preference Intensity Measurement,” Annals of Operations Research 19, 205–217. {% utility families parametric; for further comments see Bell (1988 MS) %} Farquhar, Peter H. & Yutaka Nakamura (1987) “Constant Exchange Risk Properties,” Operations Research 35, 206–214. {% %} Farquhar, Peter H. & Anthony R. Pratkanis (1993) “Decision Structuring with Phantom Alternatives,” Management Science 39, 1214–1226. {% %} Farrell, Joseph & Matthew Rabin (1996) “Cheap Talk,” Journal of Economic Perspectives 10 no. 3, 103–118. {% Properly points out the main error in the silly Lorenz et al. (2011 PNAS) paper. The letter in a diplomatic manner ignores the many silly details of Lorenz et al., but focuses on the main poiunts. %} Farrell, Simon (2011) “Social Influence Benefits the Wisdom of Individuals in the Crowd: Letter,” Proceedings of the National Academy of Sciences 108, E6256. {% Compare PT with EU for politicians; they replicate experiments by Quattrone & Tversky (1988), but now with 32 experts in politics, and they do not replicate most things, for reasons unclear. Simply, framing is volatile. They write, nicely and honestly, on p. 192: “Therefore, we must admit that our results are somehow inconclusive as we cannot offer any coherent economic, sociological or psychological theory to account for our data.” %} Fatas, Enrique, Tibor Neugebauer, & Pilar Tamborero (2007) “How Politicians Make Decisions: A Political Choice Experiment,” Journal of Economics 92, 167–196. {% %} Faulí-Oller, Ramon, Efe A. Ok, & Ignacio Ortuño-Ortín (2003) “Delegation and Polarization of Platforms in Political Competition,” Economic Theory 22, 289–309. {% law and decision theory: discusses implications of behavioral findings for law. %} Faure, Michael G. (2009) “The Impact of Behavioural Law and Economics on Accident Law,” Inaugural lecture, Erasmus University, Rotterdam, the Netherlands. {% Supports the saying “Equations reduce citations.” Eriksson (2013) finds that adding equation increases respect. %} Fawcett, Tim W. & Andrew D. Higginson (2012) “Heavy Use of Equations Impedes Communication among Biologists,” Proceedings of the National Academy of Sciences USA, Applied Mathematical Sciences 109, 11735–11739. {% real incentives/hypothetical choice: seems to consider that, and to find more risk aversion under real incentives. %} Feather, Norman T. (1959) “Subjective Probability and Decision under Uncertainty,” Psychological Review 66, 150–164. {% Seems to be considered birth of modern psychology. Seems to have proposed logarithmic perceptions. First to propose just noticeable difference as unit of cardinal measurement, according to Stigler (1950) and Luce (1958, p. 214); seems that pp. 236–237 gives utility as an example of his law. Seems that he used the method of limits, top-bottom or bottom-top, as analog of choice lists, to find subjective values. von Békésy (1947) introduced the staircase method, which is bisection, to avoid biases. %} Fechner, Gustav Th. (1860) “Elemente der Psychophysik.” Von Breitkopf und Härtel, Leipzig. 2nd edn. 1889 Reprinted 1964, Bonset, Amsterdam. Translated into English as “Elements of Psychophysics,” by Helmut E. Adler, Davis H. Howes, & Edwin G. Boring (1966), Rinehart and Winston, New York. {% Nice citations of Keynes, Knight, their differences, and de Finetti. On insurance de Finetti seems to take the usual rigid position, ignoring asymmetric information. %} Feduzi, Alberto, Jochen Runde, & Carlo Zappia (2012) “De Finetti on the Insurance of Risks and Uncertainties,” British Journal for the Philosophy of Science 63, 329–356. {% Discuss new nuance of de Finetti’s views on uncertainty and risk. %} Feduzi, Alberto, Jochen Runde, & Carlo Zappia (2014) “De Finetti on Uncertainty,” Cambridge Journal of Economics 2014, 1–21. {% Consider cases where the status-quo health state of people improves and consider health states that originally were above the status quo but are below now. They assume that utility is concave above the status quo and convex below (which, strictly speaking, is not defined for the nonquantitative outcomes considered here; but this problem can be fixed). This aspect of prospect theory, if taken in isolation, would imply that the health states considered have lower utility now than they had before. The authors test this hypothesis for 14 subjects. For 8 subjects they find higher utility now, contrary to the hypothesis, for 6 the same utility, and for 0 lower. They conclude that prospect theory is violated. It would be interesting to analyze the case considering loss aversion. Loss aversion is stronger than the concavity/convexity effect considered below. If I see things right, loss aversion will decrease the utility of outcomes that originally were closely above the status quo and now are considerably below, but will increase the utility of outcomes that originally were considerably above the status quo but now are closely below. In a complete analysis of prospect theory, also probability weighting would be incorporated. Thus, for a complete analysis of prospect theory it is not clear if the data of this paper confirm or reject it. There are also intertemporal dependencies different than prospect theory that are effective here. %} Feeny, David & Ken Eng (2006) “A Test of Prospect Theory,” International Journal of Technology Assessment in Health Care 21, 511–516. {% %} Feferman, Solomon (1989) “Infinity in Mathematics: Is Cantor Necessary?,” Philosophical Topics 17, 23–45. {% %} Fehr, Ernst (2002, January 17) “The Economics of Impatience,” Nature 415, 269–272. {% %} Fehr, Ernst (2009) “On the Economics and Biology of Trust,” Journal of the European Economic Association 7, 235–266. {% %} Fehr, Ernst, Urs Fischbacher, Bernhard von Rosenbladt, Jürgen Schupp, & Gert G. Wagner (2003) “A Nation-Wide Laboratory: Examining Trust and Trustworthiness by Integrating Behavioral Experiments into Representative Survey,” working paper. {% Classical preference model cannot explain findings. Reference dependence with loss aversion and diminishing sensitivity can. %} Fehr, Ernst & Lorenz Götte (2007) “Do Workers Work More if Wages Are high? Evidence from a Randomized Field Experiment,” American Economic Review 91, 298–317. {% Although flexible contracts dominate rigid contracts under standard assumptions, they perform worse which may be explained by workers taking contracts as reference points. %} Fehr, Ernst, Oliver Hart, & Christian Zehnder (2011) “Contracts as Reference Points—Experimental Evidence,” American Economic Review 101, 493–525. {% Edgeworth (1881): “For between the two extremes Pure Egoistic and Pure Universalistic there may be an indefinite number of impure methods; wherein the happiness of others as compared by the agent (in a calm moment) with his own, neither counts for nothing, nor yet counts for one, but counts for a fraction.” %} Fehr, Ernst & Klaus Schmidt (1999) “A Theory of Fairness, Competition and Cooperation,” Quarterly Journal of Economics 114, 817–868. {% %} Fehr, Ernst & Jean-Robert Tyran (2001) “Does Money Illusion Matter?,” American Economic Review 91, 1239–1262. {% Criticize Petersen & Winn (2014). %} Fehr, Ernst & Jean-Robert Tyran (2014) “Does Money Illusion Matter?: Reply,” American Economic Review 104, 1063–1071. {% They consider models where individual irrationality is driven out in the market, but as well models where this need not happen at all. %} Fehr, Ernst & Jean-Robert Tyran (2005) “Individual Irrationality and Aggregate Outcome,” Journal of Economic Perspectives 19, 43–66. {% Experiment shows that money illusion can affect equilibrium choice. %} Fehr, Ernst & Jean-Robert Tyran (2007) “Money Illusion and Coordination Failure,” Games and Economic Behavior 58, 246–268. {% inverse-S: confirm it both for gains and for losses, using Goldstein & Einhorn (1987) two-parameter family Risk averse for gains, risk seeking for losses: find it well confirmed. reflection at individual level for risk: they have it in their data but do not report it. Experiment in Bejing 2005 with real incentives for Chinese students (N = 153), and CEs (certainty equivalents) of 56 lotteries, using a finite mixture regression model. Stakes were like 1-hour wage (low-stake) versus 40-hour wages (high-stake). Always choice between sure outcome and 2-outcome prospect in choice lists to get CEs. Use the Goldstein & Einhorn (1987) two-parameter family for probability weighting, and power-utility. Unfortunately, they implemented two choices for real for each subject, being one for high-stake and one for low-stake (the high-low stake comparison is within-subject), giving an income effect. It will, unfortunately, amplify a contrast effect with subjects simply taking low-stakes not very seriously. Not much can be done about this (other than do between-subject). P. 154 footnote 5 properly points out that loss aversion does not affect choices between losses under PT; this paper only considers nonmixed prospects. Point out that measurements of utility and risk aversion, and investigations of whether risk aversion is decreasing or increasing and whether concavity of utility is decreasing or increasing, cannot be settled properly if there is no correction for probability weighting and other things. Find increase in relative risk aversion for gains, but find that this is primarily driven by different probability weighting for high outcomes than for low. The latter entails a violation of prospect theory (PT falsified; probability weighting depends on outcomes). No increase or decrease but constant attitude is found for losses. Losses with real incentives are implemented in an unconventional way: for each gain-choice there was a corresponding loss-choice that consisted of first a (choice-situation-dependent!) prior endowment and then the losses-choice, such that after integration of the endowment with the loss-choice the loss-choice was the same as the gain-choice. So differences between gains and losses are a matter of framing, and this is how the authors often refer to it. Discussion of it on p. 170. P. 151 top references several studies showing that heterogenous models can be really off. They find 1/4 subjects doing EV, and 3/4 PT. %} Fehr-Duda, Helga, Adrian Bruhin, Thomas Epper, & Renate Schubert (2010) “Rationality on the Rise: Why Relative Risk Aversion Increases with Stake Size,” Journal of Risk and Uncertainty 40, 147–180. {% survey on nonEU: well on probability weighting it is. Describes many implications of nonlinear probability weighting. P. 568 penultimate para gives an unconventional interpretation of disappointment aversion as probability weighting. P. 571 Table 1 the authors take 1st order risk aversion as desideratum for nonEU (thus arguing against the smooth model although they do not mention it, focusing on risk). P. C.2, Figure 2, nicely depicts indifference curves of RDU and disappointment aversion in the probability triangle, to show their different characteristics, mosty with the DA indifference curves being linear (but not parallel), as they are for every betweenness model. P. 576 end of §3.4 mentions some aspects in which the disappointment aversion model is more tractable than RDU. P. 577 footnote 6 senses correctly that there are difficulties in identifying loss aversion, but incorrectly claims that one will have to add gain prospects to mixed prospects to do it. From mixed prospects one can entirely identify preferences over non-mixed prospects (under continuity), so nonmixed prospects cannot really help. P. 578 top rightfully criticizes power probability weighting functions. My main criticism is that they can’t accommodate inverse-S. The authors point out, right so, that it can’t accommodate the common ratio effect, so neither the certainty-effect version of it as in Allais’ common ratio paradox. But it can accommodate the certainty effect in the common-consequence effect and in that version of Allais paradox. Unfortunately, that the weighting function of T&K92 is not strictly increasing for their parameter < 0.279 is called a drawback. Every parametric family imposes restrictions on its parameters. Linear-exponential (CARA) utility U() = (1 exp()) under EU restricts its parameter values such that it is strictly increasing too (by requiring > 0). Is it a drawback that there are other parameter values ( < 0) that have it decreasing? The second drawback, that relations between elevation and inverse-S are assumed, cannnot be avoided for one-parameteric families, and a negative relation is plausible. (Its main drawback is I think that it overweights small probabilities too much. And, as the second drawback just mentioned, that two parameters are desirable to separate elevation and inverse-S, agreeing with the authors claim opening up §3.6.2 on p. 579.) P. 579 suggests that the intersection point of probability weighting may exceed 0.37. I think it usually is below. They find it to exceed in their experiments, especially with general populations. Thus they do find strong evidence for inverse-S. P. 583 2nd para nicely explains that one-nonzero prospects cannot identify utility and probability weighting (I add: because their common power is unidentfiable). Then, people may have them identifiable if they assume parameteric families that do not leave the power free. But then the functional assumed a prori, rather than the data, determine the common power of utility. Some people deliberately assumed linear utility for this purpose, not as a confusuion but deliberately. (This also often happens in intertemporal choice when estimating discounting with one-time-outcomes.) This annotated bibliography in 2013 signals the problem for Benhabib, Bisin, & Schotter (2010, p. 218 middle the estimate of power utility), Glaser, Trommershäuser, Mamassian, & Maloney (2012, Psychological Science), and Zeisberger, Vrecko, & Langer (2012, see Figure 1). Download 7.23 Mb. Share with your friends:
|