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| §2.2 cites the circle test for measuring other-regarding attitude. You choose a point on a circle with center (0,0) and radius 1, say. Then the first coordinate is your payment, and the second is your opponent’s. At your maximally selfish point, (1,0), the exchange rate is . The direction of your point shows your degfree of selfishness. Pretty! %}
Linde, Jona & Joep Sonnemans (2012) “Social Comparison and Risky Choices,” Journal of Risk and Uncertainty 44, 45–72. {% %} Lindenstrauss, Joram (1966) “A Short Proof of Lyapunov’s Convexity Theorem,” Journal of Mathematics and Mechanics 15, 971–972. {% utility = representational?: §10.13, last line of third-to-last para expresses, unfortunately, the view that the only criterion for rationality is preference coherence. I criticize this view by comparing with a logician claiming that the only mistake an astronomer can make is violating the rules of logic, in my review of this book in Wakker (1986). %} Lindley, Dennis V. (1984) “Making Decisions.” Wiley, London. {% P. 1: “But above all he was a revolutionary, in the sense of Kuhn (1970), a man who replaced the accepted paradigm of inference by another, without, at first, realising what he had done.” Pp. 1-2: a distance space is embeddable in Euclidean space iff every four points are. P. 6 l. 3, on Savage (1954): “the last part was a failure” Pp. 7 & 9 explain that Savage came to understand hhe likelihood principle only quite after 1954. P. 9 emphasizes the importance of using economic decision theory to provide a rationality basis for statistical inference, citing Savage on it. P. 10 is on the optimal stopping rule discussions. P. 11 2nd para explains why the influential Edwards, Lindman, & Savage (1963) was not more influential than it was. P. 19 end of penultimate para mentions that Fisher both advocated in criticized sufficiency. %} Lindley, Dennis V. (1980) “L.J. Savage—His Work in Probability and Statistics,” Annals of Statistics 8, 1–24. {% %} Lindley, Dennis V. (1982) “Scoring Rules and the Inevitability of Probability,” International Statistical Review, 1–26. {% %} Lindley, Dennis V. (1985) “Making Decisions.” Wiley, New York. {% foundations of statistics; usual story, starting from Wald; usual discussants. %} Lindley, Dennis V. (1990) “The 1988 Wald Memorial Lectures: The Present Position in Bayesian Statistics,” Statistical Science 5, 44–89. {% On upper/lower probability: “One is that it is not necessary to increase complexity by including two numbers, upper and lower probability, in place of a single probability.” A similar point is in Camerer & Weber (1992, p. 346). %} Lindley, Dennis L. (1996) “Discussion of Walley (1996)” Journal of the Royal Statistical Society B 58, 47–48. {% probability elicitation; on how to correct inconsistencies in probability judgments, either through improving internal consistency or through external source %} Lindley, Dennis V., Amos Tversky, & Rex V. Brown (1979) “On the Reconciliation of Probability Assessments,” Journal of the Royal Statistical Society A 142, 146–180. {% According to Seidl (2002) the first discoverer of preference reversals. %} Lindman, Harold R. (1965) “The Measurement of Utilities and Probabilities.” Ph.D. dissertation, University of Michigan. {% According to Seidl (2002) the first discoverer of preference reversals through his 1965 Ph.D. disseration, so, preceding Lichtenstein & Slovic (1968, 1971) %} Lindman, Harold R. (1971) “Inconsistent Preferences among Gambles,” Journal of Experimental Psychology 89, 390–397. {% %} Lindman, Harold R. & James Lyons (1978) “Stimulus Complexity and Choice Inconsistency among Gambles,” Organizational Behavior and Human Decision Processes 21, 146–159. {% Shows that if we take expected utility but have initial wealth and income as separate arguments in deviation from final wealth, then we have preference reversals. Can be taken to support my opinion that EU of income is a big breakaway from classical models. A model accommodating preference reversals isn’t anywhere near a classical rational model. %} Lindsay, Luke (2013) “The Arguments of Utility: Preference Reversals in Expected Utility of Income Models,” Journal of Risk and Uncertainty 46, 175–189. {% foundations of statistics; discussion in Amsterdam with Molenaar and de Leeuw %} Linssen, H. Nico (1984) “Fiduciële Statistiek,” Kwantitatieve Methoden 13, 31–41. {% %} Linville, Patricia W. & Gregory W. Fischer (1991) “Preferences for Separating or Combining Events,” Journal of Personality and Social Psychology 60, 5–23. {% Refers to Hintikka (1975) for the term “impossible possible worlds” as state of the world that is subjectively possible but for omniscient perfect logician would not be possible, e.g. that 10,000 digit of square-root-2 is 1 (which it is not if I understood the text right). %} Lipman, Barton L. (1999) “Decision Theory without Logical Omniscience: Toward an Axiomatic Framework for Bounded Rationality,” Review of Economic Studies 66, 339–361. {% %} Lippman, Steven A. (1975) “On Dynamic Programming with Unbounded Rewards,” Management Science 21, 1225–1233. {% Z&Z %} Lippman, Steven A. & John C. McCall (1981) “The Economics of Uncertainty: Selected Topics and Probabilistic Methods.” In Kenneth J. Arrow & Michael D. Intriligator (eds.) Handbook of Mathematical Economics I, Ch. 5, 211–284, North-Holland, Amsterdam. {% Discounted utility model doesn’t always truly represent prefs and, hence, recommends “scenario” analysis (global EU evaluation) %} Lipscomb, Joseph (1989) “Time Preference for Health in Cost-effectiveness Analysis,” Medical Care 27, S233–S253. {% %} Lismont, Luc & Philippe Mongin (1994) “On the Logic of Common Belief and Common Knowledge,” Theory and Decision 37, 75–106. {% %} Lismont, Luc & Philippe Mongin (1995) “Belief Closure: A Semantics of Common Knowledge for Modal Propositional Logic,” Mathematical Social Sciences 30, 127–153. {% free-will/determinism: distinguishes between physical and agential possible. The latter is broader. %} List, Christian (2014) “Free Will, Determinism, and the Possibility of Doing Otherwise,” Noûs 48, 156–178. {% %} List, Christian & Clemens Puppe (2009) “Judgment Aggregation: A Survey.” In Paul Anand, Prastanta Pattanaik, & Clemens Puppe (Eds.), Handbook of Rational and Social Choice, Ch. 19, Oxford University Press, Oxford. {% Shows preference reversal for people buying sportscard. Nice thing is that these were real people really buying those cards. The author argues that, because his data do not consider risky choices, there are also problems with the classical decision paradigm outside of nonexpected utility. His interpretations of the findings are on p. 1641: “these findings should lend new insights into nonexpected utility resolutions to paradoxes of choice.” “a reevaluation of the fundamental building blocks of utility theory is necessary.” “Overall, these empirical results should have practical significance for economic theorists, empirical researchers, policy makers, and the growing body of scientific research that uses experimental methods.” %} List, John A. (2002) “Preference Reversals of a Different Kind: The “More Is Less” Phenomenon,” American Economic Review 92, 1636–1643. {% Inexperienced subjects in the market place exhibit loss aversion (endowment effect) as prospect theory has it. If subjects acquire experience, the effect attenuates. They then also exhibit less loss aversion in different tasks, i.e., transference of behavior. %} List, John A. (2004) “Neoclassical Theory versus Prospect Theory: Evidence from the Marketplace,” Econometrica 72, 615–625. {% A pretty experiment disentangling prosocial behavior, reciprocal behavior, reputation building, and field versus lab. %} List, John A. (2006) “The Behaviorist Meets the Market: Measuring Social Preferences and Reputation Effects in Actual Transactions,” Journal of Political Economy 114, 1–37. {% concave utility for gains, convex utility for losses: tests Hicksian compensating surplus and finds that inexperienced agents exhibit diminishing sensitivity and, thus, convex utility for losses as predicted by prospect theory and contrary to classical theories, but for experienced agents it is the other way around. He does this using real-market data. %} List, John A. (2006) “Using Hicksian Surplus Measures to Examine Consistency of Individual Preferences: Evidence from a Field Experiment,” Scandinavian Journal of Economics 108, 115–134. {% real incentives/hypothetical choice %} List, John A. & Craig A. Gallet (2001) “What Experimental Protocol Influence Disparaties between Actual and Hypothetical States Values? Evidence from a Meta-Analysis,” Experimental and Resource Economics 20, 241–254. {% losses from prior endowment mechanism: this is what they do. Do the traditional probability triangle with CEOs and students. Find deviations from EU primarily for small probabilities, as is common. They argue that small probabilities at catastrophes are important in policy decisions, and that cost-benefit analyses are virtually always based on EU. So, what they are finding implies that people are willing to pay much more for avoiding such risks than commonly thought. The implementation of high losses in the experiment is $100 for CEOs and $10 for students. To be sure that these can be qualified as considerable losses they asked the participants, who confirmed (p. 116 1st column l. 4), so they are solid on this point. %} List, John A. & Charles F. Mason (2011) “Are CEOs Expected Utility Maximizers?,” Journal of Econometrics 162, 114–123. {% A phantasy-story: in 300 years from now, when people use only computers to write and no-one uses pens or pencils anymore, someone will rediscover pencils, and everyone will find it a large improvement over computers. This paper reminds me of the phantasy-story. In experiments in the past, indifference-switching values were elicited through binary choices in paper-and-pencil questions. Later, computers were used and things became more sophisticated. This paper considers, as if new, the return to paper-and-pencil questions. %} Littenberg, Benjamin, Steven Partilo, Anita Licata, & Michael W. Kattan (2003) “Paper Standard Gamble: The Reliability of a Paper Questionnaire to Assess Utility,” Medical Decision Making 23, 480–488. {% P. 49 (citation from Sen): “The new [Samuelson’s revealed preference] formulation is scientifically more respectable [since] if an individual’s behavior is consistent, then it must be possible to explain the behavior without reference to anything other than behavior” %} Little, Ian M.D. (1949) “A Reformulation of the Theory of Consumer’s Behaviour,” Oxford Economic Papers 1, 90–99. {% %} Little, Ian M.D. (1985) “Robert Cooter and Peter Rappoport, ‘Were the Ordinalists Wrong about Welfare Economics?’: A Comment,” Journal of Economic Literature 23, 1186–1188. {% DOI: 10.1111/itor.12203 %} Little, John D.C. (2015) “Obituary John F. Nash Jr.,” International Transactions in Operational Research 22, 1117–1118. {% People are less ambiguity averse when choosing the better of two options than when rejecting the worst of two options. The author discusses this finding extensively. %} Liu, Hsin-Hsien (2011) “Task Formats and Ambiguity Aversion,” Journal of Behavioral Decision Making 24, 315–330. {% Prevention-focused people (focusing on cons) are more ambiguity averse than promotion-focused people. %} Liu, Hsin-Hsien (2011) “Impact of Regulatory Focus on Ambiguity Aversion,” Journal of Behavioral Decision Making 24, 412–430. {% P. 278 shows that the Samuelson colleague example does not violate EU if not the single rejection is imposed in all situations. All choices hypothetical … Do choice under risk and ambiguity (Ellsberg urn and market choice with 40-80% success chance indicated). Do single choice and repeated (twice). In repeated choice less ambiguity aversion. This is the simple finding of this paper. A plausible and even normative explanation, not mentioned by the authors, is that for repeated ambiguous choice one can learn about (unknown) probabilities in later choices from the first choice. In the Ellsberg experiment subjects were told that on each choice the computer anew determined the composition of the unknown urn, but we can then still learn about the computer from repeated choice. The authors put up loss aversion as explanation. I do understand that for repeated choice sometimes the probability of a loss is smaller than for single choice, but not why that reduces ambiguity aversion. %} Liu, Hsin-Hsien & Andrew M. Colman (2009) “Ambiguity Aversion in the Long Run: Repeated Decisions under Risk and Uncertainty,” Journal of Economic Psychology 30, 263–516. {% Whereas for moderate-outcome events with nonlow frequencies probabilities are known, for rare events of extreme magnitudes (p. 132 1/3) they are not. Hence, adding additional ambiguity aversion and ambiguity-premium (the paper calls it uncertainty premium) can help explain asset prices. So it does. Especially for options out of the money, which are very sensitive to rare events (unlike equity for instance, see p. 146), this works well. The effect is independent of risk aversion (they assume EU for given probabilities and, hence, risk aversion = concave utility). For instance, Eq. 27 (p. 143) displayes the additional component in the equity premium. They explain that recursive utility cannot do it because it should be only for rare events and recursive utility does it for all events. For example, p. 135: “In particular, the rare-event premium component, which is linked directly to rare-event uncertainty in our setting, cannot be generated by the recursive utility.” Reiterated on p. 139 P. 135 footnote 8: “We show that recursive utility cannot resolve the smile puzzle. … In effect, it does not have the additional coefficient to control the market price of rare events separately from the market price of diffuse shocks.” ambiguity seeking for unlikely: the paper does not show that, but it does show that ambiguity attitude is different for events of different likelihoods. Also it supports event/utility driven ambiguity model: event-driven.) P. 137 footnote 11: impossible events get weight 0. (As in neo-additive models.) Helps explain equity premium puzzle and volatility smile (or smirk, which is a skewed version; see p. 150). P. 152: points out that their model can only work because they add a new dimension: “since we add a new dimension to the problem: rare events and uncertainty aversion only toward rare events.” This is nice support for likelihood insensitivity, and against universal ambiguity aversion. P. 155: “these restrictions do become important as we apply the model to a range of securities with varying sensitivity to rare events.” %} Liu, Jun, Jun Pan, & Tan Wang (2005) “An Equilibrium Model of Rare-Event Premia and Its Implication for Option Smirks,” Review of Financial Studies 18, 131–164. {% Variation of the Luce-Fishburn axiomatization, using joint receipt. %} Liu, Liping (2003) “A Note on Luce-Fishburn Axiomatization of Rank-Dependent Utility,” Journal of Risk and Uncertainty 28, 55–71. {% Redefines downside risk increase as a change preferred by all decision makers with decreasing absolute risk aversion. Provides an alternative definition in terms of more prudent, improving a Keenan & Snow definition, e.g. in being transitive. All is under EU. %} Liu, Liqun & Jack Meyer (2012) “Decreasing Absolute Risk Aversion, Prudence and Increased Downside Risk Aversion,” Journal of Risk and Uncertainty 44, 243–260. {% A variation of the Pratt-Arrow measure or risk aversion where the denominator is the derivative of U at some prespecified point. Ross (1981) is central. %} Liu, Liqun & Jack Meyer (2013) “Normalized Measures of Concavity and Ross’s Strongly More Risk Averse Order,” Journal of Risk and Uncertainty 47, 185–198. {% Generalize Machina & Neilson (1987) by considering rates of substitution between different orders of riskiness. %} Liu, Liqun & Jack Meyer (2013) “Substituting one Risk Increase for Another: A Method for Measuring Risk Aversion,” Journal of Economic Theory 148, 2706–2718. {% DOI: 10.1002/bdm.1922 ambiguity seeking for unlikely: Pp. 81-82: -“To sum up, we propose that time has a differential influence on high probability and low-probability prospects. Specifically, for high probabilities, time will reduce ambiguity aversion by increasing the reliance of cognitive processing. For low probabilities, the influence of time is trivial because of the predominance of cognitive processing for small probabilities.” They use experimenter-specified probability intervals to generate probabilities (through urns with upper and lower bounds on compositions specified), taking, as usual, arithmetic midpoint as ambiguity neutrality. Study 1: Choices were hypothetical, with introspective strengths of preferences. Study 2: Matching probabilities were determined. The authors use the term ambiguity-probability trade-off task. The authors use biseparable utility, calling it -maxmin, and use as index of ambiguity aversion. Here RIS was used where 1 subject played for real, with one future payment possibly one year later. Study 3 is most interesting. A control group is like study 2 (although hypothetical). But one other group before answering the immediate questions is primed cognitively by being asked five calculation questions, and another group before answering the future-decision questions is primed affectively by first answering five affect-questions (if …, what do you feel?”). The cognitively primed indeed become more ambiguity neutral (rational!?) and the affectively primed opposite. %} Liu, Yuanyuan & Ayse Öncüler (2017) “Ambiguity Attitudes over Time,” Journal of Behavioral Decision Making 30, 80–88. {% anonymity protection %} Ljungqvist, Lars (1993) “A Unified Approach to Measures of Privacy in Randomized Response Models: A Utilitarian Perspective,” Journal of the American Statistical Association 88, 97–103. {% Gul’s (1991) disappointment aversion model extended to subjective probabilities with probabilistic sophistication. %} Lleras, Juan Sebastián (2013) “Asymmetric Gain-Loss Preferences: Endogenous Determination of Beliefs and Reference Effects,” working paper. {% Adaptive utility elicitation: find that adaptive utility measurements give higher values. %} Llewellyn-Thomas, Hilary A., Rena Arsinoff, Mary Bell, Jack Ivan Williams, & C. David Naylor (2002) “Healthy-Year Equivalents in Major Joint Replacement,” International Journal of Technology Assessment in Health Care 18, 467–484. {% adaptive utility elicitation: find that adaptive utility measurements give higher values. %} Llewellyn-Thomas, Hilary A., Heather J. Sutherland, Robert Tibshirani, Antonio Ciampi, James E. Till, & Norman F. Boyd (1982) “The Measurement of Patients’ Values in Medicine,” Medical Decision Making 2, 449–462. {% utility elicitation %} Llewellyn-Thomas, Hilary A., Heather J. Sutherland, Robert Tibshirani, Antonio Ciampi, James E. Till, & Norman F. Boyd (1984) “Describing Health States, - Methodologic Issues in Obtaining Values for Health States,” Medical Care 22, 543–552. {% Aangeraden door Lia als bekijkend verband tussen anticipated en experienced utility %} Llewellyn-Thomas, Hilary A., Heather J. Sutherland, Antonio Ciampi, Jamshid Etezadi-Amoli, Norman F. Boyd & James E. Till (1984) “The Assessment of Values in Laryngeal Cancer: Reliability of Measurement Methods,” J. Chron Disease 37, 283–291. {% %} Llewellyn-Thomas, Hilary A., Elaine C. Thiel, & R.M. Clark (1989) “Patients versus Surrogates: Whose Opinion Counts on Ethics Review Panels?,” Clinical Research 37, 501–505. {% referaat van Sylvia op 3 feb. 97. Paper suggests use of reference point idea of prospect theory but does not get into reference point-dependence. %} Llewellyn-Thomas, Hilary A., Elaine C. Thiel, & M. June McGreal (1992) “Cancer Patients’ Evaluations of Their Current Health States,” Medical Decision Making 12, 115–122. {% %} Lo, Kin Chung (1995) “Nash Equilibrium without Mutual Knowledge of Rationality,” Dept. of Economics, University of Toronto, Canada. {% equilibrium under nonEU; p. 447 takes null event in the “conservative” Savage sense %} Lo, Kin Chung (1996) “Equilibrium in Beliefs under Uncertainty,” Journal of Economic Theory 71, 443–484. {% %} Lo, Kin Chung (1996) “Weighted and Quadratic Models of Choice under Uncertainty,” Economics Letters 50, 381–386. {% PT, applications: nonadditive measures, overbidding %} Lo, Kin Chung (1998) “Sealed Bid Auctions with Uncertainty Averse Bidders,” Economic Theory 12, 1–20. {% equilibrium under nonEU; game theory for nonexpected utility; à la Gilboa & Schmeidler (1989), AA with set of priors; takes null event in Savage sense; i.e., conservative. Strict monotonicity in event E means event E is nonnull everywhere. Shows that null-invariance holds in the multiple priors model if and only if all possible prior probabilities have same support. Does AA mixing after, not before, in definition of quasi-concave; Uncertainty aversion in extensive games can lead to Pareto improvement %} Lo, Kin Chung (1999) “Extensive Form Games with Uncertainty Averse Players,” Games and Economic Behavior 28, 256–270. {% game theory for nonexpected utility %} Lo, Kin Chung (2000) “Epistemic Conditions for Agreement and Stochastic Independence of -Contaminated Beliefs,” Mathematical Social Sciences 39, 207–234. {% Shows that in the Savage model a single choice from a set of available actions can reveal violation of SEU only through violation of dominance (preference ordering on outcomes pregiven I assume). (A similar result fails for DUR, e.g. if the chosen act f is a probabilistic mixture of g and h, both of which are dominated by some other acts g' and h'.) The result is reminiscent of Wald (1950). %} Lo, Kin Chung (2000) “Rationalizability and the Savage Axioms,” Economic Theory 15, 727–733. {% game theory for nonexpected utility; considers multiple priors in game theory. %} Lo, Kin Chung (2007) “Sharing Beliefs about Actions,” Mathematical Social Sciences 53, 123–133. {% %} Lobel, Jules & George F. Loewenstein (2005) “Emote control: The Substitution of Symbol for Substance in Foreign Policy and International Law,” Chicago Kent Law Review 80, 1045–1090. {% %} Lobo, Miguel Sousa & Dai Yao (2010) “Human Judgement is Heavy Tailed: Empirical Evidence and Implications for the Aggregation of Estimates and Forecasts,” INSEAD, Fontainebleau, France. {% Elaborates on all standard sufficiency implications concerning risk aversion and the like for RDU %} Loehman, Edna (1994) “Rank Dependent Expected Utility: Stochastic Dominance, Risk Preference, and Certainty Equivalence,” Journal of Mathematical Psychology 38, 159–197. {% An individual nonparametric estimation of RDU, PT, and other things is presented, based on pairwise choices between gambles, for N=21 participants. No real incentives were used. The way of getting nonparametric fittings resembles the method of Gonzalez & Wu (1999). That is, outcomes 300, 200, 150, 100, 50, 0, -50, -100, -200 are considered and the utilities of these outcomes are treated as parameters to be estimated. Similarly, probabilities .10, .25, .50, .75, .90 are taken and their weighting function values (possibly different for gains than losses) are treated as parameters to be estimated. P. 293 brings up the interpretation as parametric fitting of piecewise linear functions. Note that the Gonzalez & Wu paper had been around long before publication, with Gonzalez presenting it in a Mathematical Psychology conference of ’92. The numerical algorithm, explained on p. 293, is iterative, again similar to Gonzalez & Wu (1999). First w(.5) = .5 is taken and then from a number of .5 prob gamble prefs (“Set I”) utility is estimated. Next these utilities are taken as given and from other gamble-prefs (“set II”) the weights of the probabilities considered are estimated. The resulting weight of probability .5, which is usually different from .5, is used to re-estimate the utilities based on set-I-prefs. These are used to recalculate the weighting function, until the process converges. At each step, the solution closest to linear is chosen. The gambles have either one nonzero outcome or one positive and one negative outcome. Here: PT: original (1979) prospect theory, which is in fact PT with reflection in the domain considered. EURDP: what I call RDU. SDM: on this domain it is in fact PT. (It is taken from Currim & Sarin 1989.) The most pronounced effect in the data is, remarkably, never noted or discussed in the paper! It is loss aversion. That is, the slope of utility is big just below zero and then strongly drops when passing through zero. This effect can be seen in Tables 4 and 5 in the slope-of-utility tables, given for eight “smooth” participants chosen out of a total of N=21. concave utility for gains, convex utility for losses: the paper finds concave utility for losses, in deviation from the commonly found convex utility. This finding may be explained because losses are framed as insurance questions which is known to enhance risk aversion. The paper finds concave utility for small gains which may result from loss aversion. The paper finds convex utility for large gains (100-300 I guess) which is harder to explain. (Maybe a numerical effect of overmodeling loss aversion so coming up with overly small slopes just after zero?) inverse-S: p. 289 says that insurance was accepted mostly for small-prob-high-losses. P. 295 finds inverse-S for RDU which is the special case of PT where weighting for gains is dual to weighting for losses (loss aversion is captured in curvature of utility). P. 296 bottom mentions the results for PT (called SDM) briefly with no clear pattern. %} Loehman, Edna (1998) “Testing Risk Aversion and Nonexpected Utility Theories,” Journal of Economic Behavior and Organization 33, 285–302. {% time preference; decreasing/increasing impatience: finds counter-evidence against the commonly assumed decreasing impatience and/or present effect, explaining it by the value of anticipation and savoring. Seems to find negative discounting for losses. %} Loewenstein, George F. (1987) “Anticipation and the Value of Delayed Consumption,” Economic Journal 97, 666–684. {% %} Loewenstein, George F. (1988) “Frames of Mind in Intertemporal Choice,” Management Science 34, 200–214. {% P. 31, bottom: argues that economists should pay more attention to psychological aspects of time preference. %} Loewenstein, George F. (1992) “The Fall and Rise of Psychological Explanations in the Economics of Intertemporal Choice.” In George F. Loewenstein & John Elster (1992) Choice over Time, 3–34, Russell Sage Foundation, New York. {% real incentives/hypothetical choice: §5 argues that real monetary incentives are not very important, because often other incentives than monetary are more important (status, being best, other emotions). Paper discussed behavioral economics versus experimental economics. The author sometimes gets carried away with his enthusiasm for behavioral economics and against experimental economics. Such as footnote 2 (p.F31): “Because context cannot be eliminated, experiments should never be used for the purpose of measuring individual propensities. … Some EE’s [experimental economists] seem to believe they know the answer: whatever context gives results that are closest to the standard economic model.” Or the final sentence of the paper: “Given that BEs [behavioral economists] have proposed some of the most novel and provocative hypotheses about individual behaviour, BE may well be the single best application of EE [experimental economics] methods.” Download 7.23 Mb. Share with your friends:
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