Bibliography
real incentives/hypothetical choice, explicitly ignoring hypothetical
Download 7.23 Mb.
|
real incentives/hypothetical choice, explicitly ignoring hypothetical: p. 182 l. -9 writes that the authors only cite experiments with real incentives, and in this sense the priority claims of this paper are unreliable. P. 183 writes, on their method: “we propose and test a new method.” In an email of 13 Feb., 2011, I pointed out to the authors that Takeuchi (2011) had used this method for measuring discounting before. So the authors now cite him om p. 182 last para: “Takeuchi (2011) uses an alternative procedure to estimate discount rates that is theoretically invariant to utility curvature …” The authors consider correcting for probability weighting, but it does not do much. One reason can be that they use the T&K 92 family, which has mostly the inverse-S component, whereas here the pessimism component is more relevant. Another reason can be that discounting and probability weighting have much collinearity. P. 190, end of §2.1: because the authors use real incentives, the longest time period they can consider is 12 weeks. (real incentives/hypothetical choice: for time preferences) %} Laury, Susan K., Melayne Morgan McInnes, & J. Todd Swarthout (2012) “Avoiding the Curves: Direct Elicitation of Time Preferences,” Journal of Risk and Uncertainty 44, 181–217. {% value of information; normal/extensive form %} LaValle, Irving H. (1968) “On Cash Equivalents and Information Evaluation in Decisions under Uncertainty, Part I: Basic Theory, Part II: Incremental Information Decisions, Part III: Exchanging Partition-J for Partition-K Information,” Journal of the American Statistical Association 63, 252–276, 277–284, 285–290. {% simple decision analysis cases using EU: §1.5 (p. 6) has many nice examples, revisited later (fig. 2.16, Example 4.4). Example 4.3.1 (p. 165) and §4.7 (p. 179) have more. %} LaValle, Irving H. (1978) “Fundamentals of Decision Analysis.” Holt, Rinehart, Winston, New York. {% value of information; normal/extensive form %} LaValle, Irving H. (1980) “On Value and Strategic Role of Information in Semi-Normalized Decisions,” Operations Research 28, 129–138. {% small worlds; dynamic consistency; assumes that acts, conditional upon any event, can be ordered in a way independent of anything else. Mainly this assumption implies independence (compare p. 123, fourth paragraph) %} LaValle, Irving H. (1992) “Small Worlds and Sure Things: Consequentialism by the Back Door.” In Ward Edwards (ed.) Utility Theories: Measurement and Applications, 109–136, Kluwer Academic Publishers, Dordrecht. {% normal/extensive form %} LaValle, Irving H. & Peter C. Fishburn (1987) “Equivalent Decision Trees and Their Associated Strategy Sets,” Theory and Decision 23, 37–63. {% %} LaValle, Irving H. & Peter C. Fishburn (1991) “Lexicographic State-Dependent Subjective Expected Utility,” Journal of Risk and Uncertainty 4, 251–269. {% %} Lavalle, Irving H. & Peter C. Fishburn (1992) “State-Independent Subjective Expected Lexicographic Utility,” Journal of Risk and Uncertainty 5, 217–240. {% %} LaValle, Irving H. & Peter C. Fishburn (1996) “On the Varieties of Matrix Probabilities in Nonarchimedean Decision Theory,” Journal of Mathematical Economics 25, 33–54. {% dynamic consistency: favors abandoning time consistency, so, favors sophisticated choice; assumes the other conditions implicitly. It appears from their analysis of violation of independence that they consider sophisticated choice as self-evident; The strategic analysis assumes choice prior to the resolution of uncertainty (at least, if in the third paragraph of p. 383 “evaluate his or her position prior to the occurrence or nonoccurrence of uncertainty” can be identified with prior choice, which the subsequent text indeed suggests; if not then the paper is ambiguous), and does Alias (b) => (c). So (1) => (a) (forgone-branch independence; often called consequentialism), (a) => (b) (part of DC), and (c) => (1) (RCLA) are assumed implicitly. %} LaValle, Irving H. & Kenneth R. Wapman (1986) “Rolling Back Trees Requires the Independence Axiom,” Management Science 32, 382–385. {% value of information; Value of informatie for Choquet Expected Utility %} LaValle, Irving H. & Yongsheng Xu (1990) “Information Evaluation under Nonadditive Expected Utility,” Journal of RIsk and Uncertainty 3, 261–275. {% %} Law, John (1705) “Money and Trade Considered, with a Proposal for Supplying the Nation with Money.” In Antoine E. Murphy (ed., 1997) Monetary Theory, Vol. 5. Routledge, London. {% Historical discussions of the roots of the risk-uncertainty distinction %} Lawson, Tony (1985) “Uncertainty and Economic Analysis,” Economic Journal 95, 909–927. {% Outcomes are minutes of sexual activity, hypothetical that is. They find usual patterns of hyperbolic discounting. %} Lawyer, Steven R., Sonja A. Williams, Tereza Prihodova, Jason D. Rollins, & Anita C. Lester (2010) “Probability and Delay Discounting of Hypothetical Sexual Outcomes,” Behavioural Processes 84, 687–692. {% %} Layard, Richard (2005) “Happiness, Lessons from a New Science.” Penguin, London. {% %} Lazaro, Angelina, Ramon Barberan, & Encarnacion Rubio (2002) “The Discounted Utility Model and Social Preferences: Some Alternative Formulations to Conventional Discounting,” Journal of Economic Psychology 23, 317‑337. {% %} Lazimy, Rafael (1986) “Solving Multiple Criteria Problems by Interactive Decomposition,” Mathematical Programming 35, 334–361. {% %} Lazzarini, Sergio G., Regina Madalozzo, Rinaldo Artes, & José de Oliveira Siqueira (2005) “Measuring Trust: An Experiment in Brazil,” Brazilian Journal of Applied Economics 9, 153–169. {% Critical discussion of Savage (1954), still calling his theorem beautiful. P. 142 has a nice text on probabilities through analogies with benchmark random mechanisms, with is similar to matching probabilities although there is no subjective twist: “Since the classical theory is essentially mathematical and clearly not normative it is rather unconcerned about how one interprets the probability measures P. The easiest interpretation is probably that certain experiments such as tossing a coin, drawing a ball out of a bag, spinning a roulette wheel, etc., have in common a number of features which are fairly reasonably described by probability measures. To elaborate a theory or a model of a physical phenomenon in the form of probability measures is then simply to argue by analogy with the properties of the standard 'random' experiments.” %} Le Cam, Lucien (1977) “A Note on Metastatistics or ‘An Essay toward Stating a Problem in the Doctrine of Chances’,” Synthese 36, 133–160. {% Ch. 1.6, p. 11-15 %} Le Cam, Lucien (1986) “Asymptotic Methods in Statistical Decision Theory.” Springer, Berlin. {% Utility of gambling %} Le Menestrel, Marc (2001) “A Process Approach to the Utility of Gambling,” Theory and Decision 50, 249–262. {% %} Le Menestrel, Marc & Bertrand Lemaire (2004) “Biased Extensive Measurement: the Homogeneous Case,” Journal of Mathematical Psychology 48, 9–14. {% Recursive utility à la Koopmans. Generalize earlier results on recursive utility regarding unbounded utility and some results of additive separability still holding in their non-separable model. %} Le Van, Cuong & Yiannis Vailakis (2005) “Recursive Utility and Optimal Growth with Bounded or Unbounded Returns,” Journal of Economic Theory 123, 187–209. {% %} Leaf, Alexander (1989) “Cost Effectiveness as a Criterion for Medicare Coverage,” New England Journal of Medicine 321, 898–900. {% Seem to have tested risk attitudes for money and for time (I guess not life duration but waiting time. And not waiting time in sense of delayed payment where discounting would come in, but waiting time in sense of time lost, as with traffic for instance. Probably hypothetical choice. Seems more risk seeking for monetary losses than for time losses. (Risk averse for gains, risk seeking for losses.) %} Leclerc, France, Bernd H. Schmitt, & Laurette Dube (1995) “Waiting Time and Decision Making: Is Time like Money?,” Journal of Consumer Research 110–119. {% game theory for nonexpected utility: calculate equilibria in several games, and show that nonexistence is possible. %} Leclerc, Philip & Jason Merrick (2013) “Equilibria in Finite Games with CPT Preferences,” working paper. {% foundations of probability: according to Miettinen (2001), this is a seminal paper arguing for the use of probabilities and Bayes formula in epidemiology. %} Ledley, Robert S. & Lee.B. Lusted (1959) “Reasoning Foundations of Medical Diagnosis,” Science 130, 9–21. {% %} Ledyard, John O. (1971) “A Pseudo-Metric Space of Probability Measures and the Existence of Measurable Utility,” Annals of Mathematical Statistics 42, 794–798. {% Does experiments with several choices, studying the effects of prior outcomes on later choices. decreasing ARA/increasing RRA: if repeated payments of every choice, then decreasing absolute risk aversion. random incentive system: finds it confirmed, where it removes income effects as occurring with repeated payment. Nice study! %} Lee, Jinkwon (2008) “The Effect of the Background Risk in a Simple Chance Improving Decision Model,” Journal of Risk and Uncertainty 36, 19–41. {% measure of similarity %} Lee, Michael D. (2001) “Determining the Dimensionality of Multidimensional Scaling Models for Cognitive Modeling,” Journal of Mathematical Psychology 45, 149–166. {% probability elicitation; good background for werk of Daniëlle Timmermans; explains Lens model. %} Lee, Ju-Whei & J. Frank Yates (1992) “How Quantity Judgment Changes as the Number of Cues Increases: An Analytical Framework and Review,” Psychological Bulletin 112, 363–377. {% inverse-S: p. 61 seems to support that %} Lee, Wayne (1971) “Decision Theory and Human Behavior.” Wiley, New York. {% %} Leeds, Stephen (1990) “Discussion: Levi’s Decision Theory,” Philosophy of Science 57, 158–168. {% %} Lefebvre, Mathieu & Ferdinand M. Vieider (2013) “Reining in Excessive Risk-Taking by Executives: the Effect of Accountability,” Theory and Decision 75, 497–517. {% %} Lefebvre, Mathieu & Ferdinand M. Vieider (2014) “Risk Taking of Executives under Different Incentive Contracts: Experimental Evidence,” Journal of Economic Behavior and Organization 97, 27–36. {% ratio bias: find that it easily disappears with good incentives, avoidance of contrast effects, and other things. %} Lefebvre, Mathieu, Ferdinand M. Vieider, & Marie Claire Villeval (2011) “The Ratio Bias Phenomenon: Fact or Artifact?,” Theory and Decision 71, 615–641. {% %} Lefoll, Jean, Jean M. Guiot, & Alain Chateauneuf (1988) “Allais’ Model vs. Expected Utility: Some Preliminary Empirical Results,” paper presented at Foundations of Utility and Risk theory IV conference at Budapest, June 6–10, 1988. {% Utilty of agent is known but (possibly nonadditive) belief is not. Two experts guess the belief of the agent. Definitions of better guesses are given and analyzed. %} Lefort, Jean-Philippe (2009) “Guessing the Beliefs,” Journal of Mathematical Economics 45, 846–853. {% Side-issue: existence of God %} Lefton, Brian (1990) “Is God an Abstract Object?,” Noûs 24, 581–598. {% %} Légaré, France, Annette M. O’Connor, Ian D. Graham., Georges A. Wells, & Stéphane Tremblay (2006) “Impact of the Ottawa Decision Support Framework on the Agreement and the Difference between Patients’ and Physicians’ Decisional Conflict,” Medical Decision Making 26, 373–390. {% Dollar-cost Averaging: invest 5% of your money every month. Not optimal from the prespective of classical theories. Neither from the perspective of prospect theory and loss aversion, as this paper analyzes and tests on data. So, it is a negative finding. %} Leggio, Karyl B. & Donald Lien (2001) “Does Loss Aversion Explain Dollar-Cost Averaging?,” Financial Services Review 10, 117–127. {% foundations of statistics %} Lehmann, Erich L. (1950) “Some Principles of the Theory of Testing Hypotheses.” In Omar F. Hamouda & J.C. Robin Rowley (1997, eds.) “Statistical Foundations for Econometrics.” Edward Elgar, Cheltenham. {% foundations of statistics %} Lehman, Erich L. (1958) “Significance Level and Power.” In Omar F. Hamouda & J.C. Robin Rowley (1997, eds.) “Statistical Foundations for Econometrics.” Edward Elgar, Cheltenham. {% %} Lehmann, Erich L. (1986) “Testing Statistical Hypotheses.” Wiley, New York. {% %} Lehmann, Erich L. (1990) “Model Specification: The Views of Fisher and Neyman, and Later Developments,” Statistical Science 5, 160–168. {% foundations of statistics %} Lehmann, Erich L. (1993) “The Fisher, Neyman-Pearson Theories of Testing Hypotheses: One Theory or Two?,” Journal of the American Statistical Association 88, 1242–1249. {% Dutch book %} Lehman, R. Sherman (1955) “On Confirmation and Rational Betting,” Journal of Symbolic Logic 20, 251–262. {% A forecaster and an inspector play a game, observing one by one realizations x1, x2, …of a distribution. Whatever the checking rule used by the inspector, the forecaster can manipulate. Manipulation means that he makes a forecast after observing x1,…,xn for each n, not using any prior knowledge and using only x1,…,xn, such that he is perfectly calibrated in the sense that asymptotically every relative frequency in his predictions match the true relative frequencies. The idea is that the infinite sequence x1,…,xn contains enough info, if observed long enough, to get that done, without needing prior knowledge. %} Lehrer, Ehud (2001) “Any Inspection is Manipulable,” Econometrica 69, 1333–1347. {% updating; nice idea to do updating under nonadditive measures analogous to conditional expectations theory. Anomalies can still occur. Can be excluded in a somewhat ad hoc way by excluding them by restricting set of sub-sigma-algebra-measurable functions accordingly. In traditional additive probability theory, E(f|A), the conditional expectation of a rv f given a sigma-field A is the “averaging out” of f over A. It is the function g that is A measurable and, given that, minimizes expectation of quadratic difference with f. Conditional expectation of rv is the primitive concept to think of. Conditional probability of event B given event C is derived concept, as follows: (1) Take 1B as rv and {C,Cc} as sigma-algebra. You can see only through {C,Cc}. Conditional probability of B is then what you see of 1B in event C. In general, it can be proved that E(f|A) has the same expectation as f, in fact it has that over every A event. Also, on every A event it does not exceed max or min f. Lehrer considers extension of these concepts to nonadditive measures. The starting idea is to, again, let E(f|A) minimize quadratic difference with f. First problem: unfortunately, that does not have nice properties such as having same expectation as f, or not exceed max or min of f. So, one restricts attention to subclass of functions, measurable w.r.t. C, that do have the desired properties, and only over those one minimizes expectation of quadratic difference. Second problem: The solution need not be unique. Lehrer proposes refinements. %} Lehrer, Ehud (2005) “Updating Non-Additive Probabilities—A Geometric Approach,” Games and Economic Behavior 50, 42–57. {% Takes a variation of the Choquet integral that is always concave. It agrees if the weighting function is convex, but is a concave functional that in a way is closest if the weighting function is not convex. That is, for a prospect X and a weighting function v, min{f(X)} is taken over all concave and homogeneous functions f that dominate w for all indicator functions. A preference axiomatization is given. The same definition can be used if w is not defined for all subsets. %} Lehrer, Ehud (2009) “A New Integral for Capacities,” Economic Theory 39, 157–176. {% Characterizes a subfamily of multiple priors, with only finitely many priors. The value of an ambiguous act is the suppremum of dominated unambiguous acts, which is very pessimistic. An act is fat-free if reducing any outcome strictly worsens the act. In the other case, if there is fat, then an EU minimizing prior can be found making the relevant outcome-event null. Strong fat-free maintains fat-free under mixing with a nonminimal outcome. If two acts have fat, there can be synergy under mixing, and this is a nice way of interpreting things. The model is the decision model corresponding with the functional of Lehrer (2009). The paper applies its model to NE in game theory. %} Lehrer, Ehud (2012) “Partially Specified Probabilities: Decisions and Games,” American Economic Journal: Microecnomics 4, 70–100. {% Every simple act can be written as a weighted sum of indicator functions SUMj1Ej, where the Ejs may be nested and so on. The model of this paper (“event-separable representation”) assumes existence of a nonadditive weighting function v, and a subjective choice of one of the many possible decompositions SUMj1Ej for each act, after which it is evaluated by a separate-event weighting SUMjv(Ej). It assumes that acts 1E with only one nonzero outcome are always evaluated by v(E). If acts are subjectively similar (same events involved) they provide no hedge against each other and satisfy independence preference conditions. RDU is the special case where the events Ej are nested. %} Lehrer, Ehud & Roee Teper (2015) “Subjective Independence and Concave Expected Utility,” Journal of Economic Theory 158, 33–53. {% Realist interpretation of utility: it is concrete, an object or quality of mental state etc. Instrumental interpretation of utility: only theoretical concept. %} Lehtinen, Aki (2001) “The Interpretation of Utility Theory,” {% P. 67 2nd column ll. 1-3 does the typical overselling of DFE of suggesting that everything in life that has no known probabilities must be DFE: “had no alternative but to make decisions from experience” (italics from original). Do DFD (decision from description) versus DFE (decision from experience) for both monetary and medical outcomes. As the authors properly explain, the latter have to be hypothetical, and then for avoiding confounded comparisons the former are also better done hypothetically, which is what they did. As Figure 3 illustrates, they find, remarkably, more optimism for DFE than for DFD, but also somewhat more, rather than less, inverse-S. Strongest finding is way more inverse-S fror medical than for money. %} Lejarraga, Tomás, Thorsten Pachur, Renato Frey & Ralph Hertwig (2016) “Decisions from Experience: From Monetary to Medical Gambles,” Journal of Behavioral Decision Making 29, 67–77. {% questionnaire for measuring risk aversion: not really that, but nice alternative: people can each time decide to blow a balloon up one more or not. If they do, 1 cent is added to their gains, but each time there is a chance the balloon explodes and then all gains are lost. Probability of explosion is something like j/128 at jth trial. Pretty idea! %} Lejuez, Carl W., Jennifer P. Read, Christopher W. Kahler, Jerry B. Richards, Susan E. Ramsey, Gregory L. Stuart, David R. Strong, & Richard A. Brown (2002) “Evaluation of a Behavioral Measure of Risk Taking: The Balloon Analogue Risk Task (BART),” Journal of Experimental Psychology: Applied 8, 75–84. {% %} Leland, Jonathan W. (1994) “Generalized Similarity Judgments: An Alternative Explanation for Choice Anomalies,” Journal of Risk and Uncertainty 9, 151–172. {% measure of similarity; Seems to show that violations of stochastic dominance can be found in experiments only if the dominance relation is not transparent. %} Leland, Jonathan W. (1998) “Similarity Judgments in Choice under Uncertainty: A Reinterpretation of the Prediction of Regret Theory,” Management Science 44, 659–672. {% The author argues that violations of independence may be less important than thought, the reason given being that in matrix representation it is less violated (one can debate if the latter is due to true preference or due to heuristic). It then presents regret theory and Rubinstein-type similarity arguments, each in one page, as alternative points to pursue. The paper is nicely written, but the content is thin (just reiterating that independence is less violated in matrix format, and regret and similarity) and not new. %} Leland, Jonathan W. (2010) “The Hunt for a Descriptive Theory of Choice under Risk—A View from the Road not Taken,” Journal of Socio-Economics 39, 568–577. {% Uses Dempster-Shafer belief functions, having a separation between uncertainty and imprecision. Uncertainty seems to be qualitative and concern noise, and imprecision seems to be in imperfect discrimination of measurement instrument. %} Lelandais, Benoît, Isabelle Gardin, Laurent Mouchard, Pierre Vera, & Su Ruan (2013) “Dealing with Uncertainty and Imprecision in Image Segmentation Using Belief Function Theory,” International Journal of Approximate Reasoning 55, 376–387. {% %} Lemmer, John F. & Laveen N. Kanal (1988) “Uncertainty in Artificial Intelligence 2; Machine Intelligence and Pattern Recognition, Vol.5.” North-Holland, Amsterdam. {% Using simulations, shows that a joint estimation of risk preference and technology, something that seems to be common in agricultural risk studies, does not work well. Last sentence of intro: “by allowing researchers to discard doomed-to-fail estimation projects at an early stage.” %} Lence, Sergio H. (2009) “Joint Estimation of Risk Preferences and Technology: Flexible Utility or Futlity?,” American Journal of Agricultural Economics 91, 581–598. {% On choice lists: measure indifference values in two different ways: (a) ping-pong; (b) “titration.” In each, consider PE-SG questions where people must give the probability p making them indifferent between, for instance, being blind and (perfect health)p(death). In the titration method people are offered a decreasing sequence of probabilities p = 1, p = 0.99, … of “offers” of risks that they are willing to accept, until the point where they are no longer willing to accept the offer. That point is their indifference point. The ping-pong method “offered” risks 0.01, 0.99, 0.02, 0.98, 0.10, 0.90, 0.80, 0.20, 0.70, 0.30, 0.60, 0.40, 0.50. But surprisingly, the titration method gave higher results. %} Lenert, Leslie A., Daniel J. Cher, Mary K. Goldstein, Merlynn R. Bergen, & Alan M. Garber (1998) “The Effect of Search Procedures on Utility Elicitations,” Medical Decision Making 18, 76–83. {% 39% of the subjects ordered health states differently in pairwise choice than in the SG %} Lenert, Leslie A., Sydney Morss, Mary K. Goldstein, Merlynn R. Bergen, William O. Faustman, & Alan M. Garber (1997) “Measurement of the Validity of Utility Elicitations Performed by Computerized Interview,” Medical Care 35, 915–920. {% %} Lenert, Leslie A., Cathy D. Sherbourne, & Valerie F. Reyna (2001) “Utility Elicitation Using Single-Item Questions Compared with a Computerized Interview,” Medical Decision Making 21, 97–104. {% P. 779 refers to Gold et al. (1996) for the, I think unjustified, claim (where preference weights means utilities): “In addition, clinically obtained preference weights are ill-suited for use in CEAs of public health interventions designed to inform resource allocation in populations, where it is community, rather than patient preferences, that are relevant.” %} Lenert, Leslie A. & Roy M. Soetikno (1997) “Automated Computer Interviews to Elicit Utilities: Potential Applications in the Treatment of Deep Venous Trombosis,” Journal of the American Medical Informatics Association 4, 49–56. {% Using probability equivalents they measure utilities of health states according to the classical elicitation assumption (i.e., EU calculations). They find that people in poor health state judge states more positive on average than people in good health state. Interpret this finding as evidence in favor of prospect theory. Do not use prospect theory to calculate utilities from probability equivalent questions, but only EU. %} Lenert, Leslie A., Jonathan R. Treadwell, & Carolyn E. Schwartz, (1999) “Associations between Health Status and Utilities Implications for Policy,” Medical Care 37, 479–489. {% foundations of statistics %} Lenhard, Johannes (2006) “Models and Statistical Inference: The Controversy between Fisher and Neyman–Pearson,” British Journal for the Philosophy of Science 57, 69–91. {% %} Lensberg, Terje (1987) “Stability and Collective Rationality,” Econometrica 55, 935–961. {% loss aversion: erroneously thinking it is reflection: happens on p. 406. The authors present the Asian disease problem, explained by convex utility for losses as they properly point out (let us ignore probability weighting). Then they describe loss aversion as utility steeper for losses. Then they say that loss aversion is the desire to avoid a sure loss. They probably think that loss aversion enhances risk seeking in a choice between a sure loss and a risky prospect p(), which is incorrect because loss aversion here enhances risk aversion, i.e., preference for the sure loss (Wakker 2011 Exercise 9.3.8). Then they seem to think that the risk seeking enhanced by loss aversion (which can only affect mixed prospects) explains the Asian disease. Thus they conclude their reasoning: “In other words, the idely accepted prospect theory explains uncertainty-seeking ehavior as the result of loss aversion.” %} Leonhardt, James M., L. Robin Keller, & Cornelia Pechmann (2011) “Avoiding the Risk of Responsibility by Seeking Uncertainty: Responsibility Aversion and Preference for Indirect Agency when Choosing for Others,” Journal of Consumer Psychology 21, 405–413. {% Seems to have defined money illusion as a violation of the homogeneity postulate of demand. %} Leontief, Wassily W. (1936) “The Fundamental Assumptions of Mr. Keynes’ Monetary Theory of Unemployment,” Quarterly Journal of Economics 5, 192–197. {% %} Leontief, Wassily W. (1947) “A Note on the Interrelation of Subsets of Independent Variables of a Continuous Function with Continuous First Derivatives,” Bulletin of the American Mathematical Society 53, 343–350. {% %} Leontief, Wassily W. (1947) “Introduction to a Theory of the Internal Structure of Functional Relationships,” Econometrica 51, 361–373. {% information aversion: people who had given a blood sample could be informed if they were carriers of one of two genetic mutations that indicate susceptibility to breast cancer. Almost half (169/396) declined. %} Lerman, Caryn, Chanita Hughes, Stephen J. Lemon, David Main, Carrie L. Snyder, Carolyn Durham, Steven A. Narod, & Henry T. Lynch (1998) “What You Don’t Know Can Hurt You: Adverse Psychological Effects in Members of BRCA1-Linked and BRCA2-Linked Families Who Decline Genetic Testing,” Journal of Clinical Oncology 16, 1650–1654. {% This paper is a criticism of Rabin (2000, Econometrica). Rabin assumed that many people reject a fifty-fifty gamble +11, 10. The author calculates what the gamble would be if repeated 365 times independently. He points out that many accept such a gamble. He seems to conclude, and I do not understand, that the latter would imply that many will also accept the one-shot gamble. He derives from his conclusion that Rabin’s argument is based solely on questionnaires and experiments, and that real-world is different from the former. %} LeRoy, Stephen F. (2003) “Expected Utility: A Defense,” Economics Bulletin 7, 1–3. {% %} LeRoy, Stephen F. & Richard D. Porter (1981) “The Present-Value Relation: Tests Based on Implied Variance Bounds,” Econometrica 49, 555–574. {% Risk versus Uncertainty; historical comments. Argue that, for Knight, the case of subjective nonobjective additive probability was uncertainty and not risk. Also that Knight’s writing is confused. %} LeRoy, Stephen F. & Larry D. Singell, Jr. (1987) “Knight on Risk and Uncertainty,” Journal of Political Economy 2, 398–406. {% Tradeoff method: §8.6 uses it to characterize SEU. %} LeRoy, Stephen F. & Jan Werner (2000) “Principles of Financial Economics.” Cambridge University Press, New York. {% P. 409 criticizes Bayesianism not only for choosing exact probability, but also for choosing exact utility (up to level and unit), and wants to have not only a set of priors but also of utilities. Wants to allow for indeterminate choice. His decision theory violates independence of irrelevant alternatives (pp. 415 ff.). E-admissability of a prospect: there exists a P in the set of possible P’s and a U in the set of possible U’s such that the prospect is optimal for this P and U. I, by the way, do not find this a convincing criterion. Couldn’t one take a prospect that is never first but always a good second? %} Levi, Isaac (1974) “On Indeterminate Probabilities,” Journal of Philosophy 71, 391–418. {% Elaborates on his 1974 theory. Discusses second-order probabilities; seems to write: epistemic utility: evaluate utility independent of probabilities; P. 441-442 discusses Rasmussen report on nuclear safety. Credal probability: evaluate probabilities independently of utility: I checked on May 24 ’96 but it was not clearly there. %} Levi, Isaac (1980) “The Enterprise of Knowledge.” MIT Press, Cambridge, MA. {% Ch. 4 seems to be on free-will/determinism. Seems to have written on p. 121: “One must be committed, whether one knows it or not, to a definite credal probability function even though neither inducive logic nor the relevant contextual features furnish any reason for adopting that function rather than another.” %} Levi, Isaac (1986) “Hard Choices: Decision Making under Unresolved Conflict.” Cambridge University Press, New York. {% dynamic consistency; discursive writing. P. 94/95 is typical of the style of the author: “If Hammond is right, this position is untenable. Ordering and independence are indivisible. I think that Hammond is wrong.” Says that nothing in Savage prevents the Jeffrey interpretation, that probabilities can be assigned to future actions to some extent. %} Levi, Isaac (1991) “Consequentialism and Sequential Choice.” In Michael Bacharach & Susan Hurley (eds.) Foundations of Decision Theory, 92–122, Basil-Blackwell, Oxford. {% Apply updating models to common-value Dutch auctions. Non-probabilistic reasoning (NPR) refers to further info besides the probability update. %} Levin, Dan, James Peck, & Asen Ivanov (2016) “Separating Bayesian Updating from Non-Probabilistic Reasoning: An Experimental Investigation,” American Economic Journal: Microeconomics 8, 39–60. {% real incentives/hypothetical choice: subjects had to rate how likely it was that they would choose risk gambles (??), both hypothetically and real. %} Levin, Irwin P., Daniel P. Chapman, & Richard D. Johnson (1988) “Confidence in Judgments Based on Incomplete Information: An Investigation Using Hypothetical and Real Gambles,” Journal of Behavioral Decision Making 1, 29–41. {% Children and some adults could do risky choices (which each really paid, in some prizes) between sure prize or fifty-fifty gambles to get two prizes. Same for losses. Each choice was really paid (so repeated payments). risk seeking for symmetric fifty-fifty gambles: they find more risk seeking than risk aversion for gains, and even more risk seeking for losses. (Also for 0.2 probability gambles.) %} Levin, Irwin P. & Stephanie S. Hart (2003) “Risk Preferences in Young Children: Early Evidence of Individual Differences in Reaction to Potential Gains and Losses,” Journal of Behavioral Decision Making 16, 397–413. {% This paper reviews (and interprets) studies of framing and loss aversion, as alternative to the review by Kuhberg (1998) that they cite much. This paper received many citations. For me nonpychologist it was hard to relate to it. I am interested in two different aspects of loss aversion (of, say, size 2), that may explain it: (1) At a loss that in physical units is as big as a corresponding gain, the suffering when experiencing the loss is twice as big as the happiness when experiencing the gain. (2) For a loss that in suffering is as big as the joy is of a corresponding gain, it still weights twice as much in decisions because the decision maker pays more attention to losses. Under (1) loss aversion is part of utility, under (2) it is not. In (2) one can distinguish between this happening deliberately, with the decision maker thinking that it is rational to pay more attention to losses than to gains, and this happening psychologically, not as a deliberate act but automatically perceptionally and probably not rationally. It was not easy for me nonpsychologist to understand whether the distinctions the authors make relate to the above distinction or not. The authors distinguish three frame types of loss aversion, being risky framing, attribute framing, and goal framing. The second, attribute, is when people are asked for straight introspective evaluations without these being related to decisions. "How much do you like beef 75% lean" versus "How much do you beef 25% fat?" and subjects indicate their likings on a scale. Subjects like more the 75% lean formulation, which is not surprising as the authors point out somewhere (p. 159). For one thing, it has the same ambiguity-problem as the well known Asian disease problem (75% nonfat does not mean the other 25% has to be fat). The authors feel it necessary, p. 159 4th para, to make explicit that the above judgment does not involve risk. The third, goal framing, is, if I understand right, decision problems where one option is doing nothing. Breast self-examination is done more with negative info (not doing has decreased chance of finding tumor) than with positive (doing so has increased chance of finding tumor), p. 168 2nd para. For the first, risky framing, the authors do point out at some stage that loss aversion can and has been used also for decisions if tradeoffs do not concern getting some more with 60% probability versus some less with 40% probability but also getting some more on one attribute at the cost of getting some less on another. They do point out this is like goal framing (p. 180 top). The useful summary p. 181 2nd para also suggests so. But then why risky framing is considered a different category escapes me. Then 2nd framing of evaluation without relation to decision interests me economist less anyhow. The paper often writes in a boasting manner, praising itself (p. 177 bottom, p. 179 penultimate para "unique", p. 181 penultimate para; p. 182 last para "The discovery of the distinguishing features ..") P. 150 ll. 3-8 is funny for economists. When the authors want to show how diverse the areas are where loss aversion has appeared, they mention 7 subdisciplines of psychology and then one other discipline: business. Later for decisions also medical (and clinical!) decisions are mentioned, and bargaining, and some more, but, sorry for economics, it did not make it to the list. %} Levin, Irwin P., Sandra L. Schneider, & Gary J. Gaeth (1998) “All Frames Are not Created Equal: A Typology and Critical Analysis of Framing Effects,” Organizational Behavior and Human Decision Processes 76, 149–188. {% restricting representations to subsets: mainly one-dimensional representations without particular aggregation properties. Some results are on utilitarianism, U1(x1) + … + Un(xn) where, however, the Uj’s are used as directly observable inputs so that it is more de Finetti-type additive representations p1x1 + … + pnxn. %} Levin, Vladimir L. (2010) “On Social Welfare Functionals: Representation Theorems and Equivalence Classes,” Mathematical Social Sciences 59, 299–305. {% %} Levine, Frederic J. & Lester Luborsky (1981) “The Core Confictual Relationship Theme Method: A Demonstration of Reliable Clinical Inferences by the Method of Mismatched Cases.” In Saul Tuttmen, Carol Kaye, & Muriel Zimmerman (eds.) Object and Self: A Developmental Approach. International Universities Press; New York. {% After Math.Psy-meeting 1992 the author mailed this paper, and earlier, papers, to me. May have something to do with tradeoff consistency, and with additive representations on subsets. %} Levine, Michael V. (1982) “Fundamental Measurement of the Difficulty of Test Items,” Journal of Mathematical Psychology 25, 243–268. {% Games with incompete information, value of information %} Levine, Pierre & Jean-Pierre Ponssard (1977) “The Value of Information in Some NonZero Sum Games,” International Journal of Game Theory 6, 221–229. {% %} Levinger, George & David J. Schneider (1969) “Test of the ‘Risk is a Value’ Hypothesis,” Journal of Personality and Social Psychology 11, 165–169. {% Uses a huge data set. Bookmakers for sports are better at predicting outcomes of games, and there do not seem to be people performing systematically better than bookmakers. They deliberately set odds against known biases (and deviating from equilibrating supply and demand), such as biased in favor of favorite but against home team; someone knowing this can benefit from it. Here bookmakers can typically do what thousands of people have found out they cannot do on the stock market! %} Levitt, Steven D. (2004) “Why Are Gambling Markets Organised so Differently from Financial Markets?,” Economic Journal 114, 223–246. {% %} Levitt, Steven D. (2005) “Freakonomics.” Penguin, London {% P. 347 abstract opens with: “We can think of no question more fundamental to experimental economics than understanding whether, and under what circumstances, laboratory results generalize to naturally occurring environments.” Such a sentence is typical of researchers putting their own field forward as the most important field there is. %} Levitt, Steven D. & John A. List (2007) “Viewpoint: On the Generalizability of Lab Behaviour to the Field,” Canadian Journal of Economics 40, 347–370. {% A nice survey of the main experiments in social choice, and complications for external validity of lab experiments on them (e.g., see Table 1 p. 155). Some details that I see a bit different are: the authors suggest that external validity is no problem in the natural sciences. I conjecture that it is a bigger problem in natural sciences than in the social sciences. The authors use the term generalizability in too narrow a sense, being only for generalizability of lab findings to outside world. (p. 153). The authors only consider moral costs for subjects, but there will be other costs such as effort or loss of self-confidence. The formula (“model”) on p. 157 serves no purpose. %} Levitt, Steven D. & John A. List (2007) “What Do Laboratory Experiments Measuring Social Preferences Reveal about the Real World?,” Journal of Economic Perspectives 21, 153–174. {% Value-induced beliefs: reported probabilities are not used to describe beliefs, but to justify decisions taken, in a medical context. . %} Levy, Andrea G. & John C. Hershey (2006) “Distorting the Probability of Treatment Success to Justify Treatment Decisions,” Organizational Behavior and Human Decision Processes 101, 52–58. {% stochastic dominance survey %} Levy, Haim (1992) “Stochastic Dominance and Expected Utility: Survey and Analysis,” Management Science 38, 555–593. {% real incentives/hypothetical choice: actual payment was done at the end after dividing by 1,000 decreasing ARA/increasing RRA: accept decreasing absolute risk aversion (DARA) but find no increasing RRA (IRRA), says it’s decreasing or at best constant. Sixty-two participants had to play 10 rounds of investing, experimental amounts in order of $30,000, actual payment was done at the end after dividing by 1,000. If their game-asset became negative during the game, they had to stop and pay (ruin). That setup made the participants conservative, indeed none ended in ruin. The latter may explain the DRRA found: those with little money become very risk averse so as to avoid ruin, those with much money were lucky and, thus, are encouraged to risk more. This holds the more so as only the game-rewards, not the actual richness of the participants, played a role. Results on absolute risk aversion and RRA were derived from intermediate choices (time series) and, thus, assume the isolation effect. However, the isolation effect is not easy to defend here because the participants clearly are aware of the dynamic repeated setup, the more so as they get a sum total in the end. %} Levy, Haim (1994) “Absolute and Relative Risk Aversion: An Experimental Study,” Journal of Risk and Uncertainty 8, 289–307. {% Finds violations of stochastic dominance, but more due to randomness than systematic. Puts it forward as argument against original prospect theory of Kahneman & Tversky (1979) and in favor of new Tversky & Kahneman (1992) prospect theory and rank dependence. %} Levy, Haim (2008) “First Degree Stochastic Dominance Violations: Decision Weights and Bounded Rationality,” Economic Journal 118, 759–774. {% Link to comment on multi-publication by Levy & Levy %} Levy, Haim & Moshe Levy (2002) “Arrow-Pratt Risk Aversion, Risk Premium and Decision Weights,” Journal of Risk and Uncertainty 25, 265–290. {% Link to comment on multi-publication by Levy & Levy %} Levy, Haim & Moshe Levy (2002) “Experimental Test of Prospect Theory Value Function: A Stochastic Dominance Approach,” Organizational Behavior and Human Decision Processes 89, 1058–1081. {% Nice idea to assume that people in their instantaneous decisions go by PT value function, but after some time adapt and then their vNM utility function takes 2over. They have the two-argument function depending on current wealth and change of that. There are, unfortunately, inaccuracies in the analysis. %} Levy, Haim & Zvi Wiener (1996) “Prospect Theory and Utility Theory: Temporary and Permanent Attitude toward Risk,” Hebrew University. {% correlation risk & ambiguity attitude: Experiment 1 investigates it but finds no relation. But, as the authors point out, their sample is small (N=22). %} Levy, Ifat, Jason Snell, Amy J. Nelson, Aldo Rustichini, & Paul Glimcher (2010) “Neural Representation of Subjective Value under Risk and Ambiguity,” Journal of Neurophysiology 103, 1036–1047. {% Link to comment on multi-publication by Levy & Levy %} Levy, Moshe & Haim Levy (2001) “Testing for Risk Aversion: A Stochastic Dominance Approach,” Economics Letters 71, 233–240. {% Link to comment on multi-publication by Levy & Levy %} Levy, Moshe & Haim Levy (2002) “Prospect Theory: Much Ado about Nothing,” Management Science 48, 1334–1349. {% three-prisoners problem; Baumann (2005) argued that not switching need not be irrational. %} Levy, Ken (2007) “Baumann on the Monty Hall Problem and Single-Case Probabilities,” Synthese 158, 139–151. {% Do Holt & Laury (2002) experiment. Consistency is better in simultaneous (rather than sequential) choice and increasing or random rather than decreasing order, and much higher for 10 times higher payments and after experience. Risk aversion is higher in sequential, and in decreasing & random than in increasing. Sequential decisions give more inconsistencies than simultanous, as did decreasing rather than increasing or random orders. %} Lévy-Garboua, Louis, Hela Maafi, David Masclet, & Antoine Terracol (2012) “Risk Aversion and Framing Effects,” Experimental Economics 15, 128–144. {% Several people have argued that with common utility functions the income effect in the WTP/WTA discrepancy is too small to explain it. This paper shows that with extreme utility functions it can be. For instance, if we take logarithmic utility and let it tend to minus infinity at a status quo, then extreme things can happen. The paper also comments on Rabin’s (2000) calibration paradox, siding with Rubinstein’s (2006) view that it can be solved by taking utility of income rather than utility of final wealth. In Peter P. Wakker (2010) “Prospect Theory: For Risk and Ambiguity.” Cambridge University Press, Cambridge, UK, §8.6, I criticize this view: utility of income is not a small variation of EU, but is the same as reference dependence of prospect theory and is a major breakaway. Whereas EU is the hallmark of rationality, reference dependence is utterly irrational. %} Lewandowski, Michal (2014) “Buying and Selling Price for Risky Lotteries and Expected Utility Theory with Gambling Wealth,” Journal of Risk and Uncertainty 48, 253–283. {% %} Lewbel, Arthur & William Perraudin (1995) “A Theorem of Portfolio Separation with General Preferences,” Journal of Economic Theory 65, 624–626. {% The author, with an economic background although the style suggests more of psychology and sociology, wrote this before receiving Ph.D, and developed own ideas on ordinal revolution and history. Although I disagree with several (such as difference between ordinalism and behaviorism), the paper gave me many new insights and I enjoyed it much. Argues that ordinalism does not work because, first, it does not get good data (I agree) and, second, it ignores sociological (institutional) effects (not my focus of research). P. 1294 §B: I don’t think there was an attack by psychologists on marginal utility. The attack was initiated by the other side. conservation of influence: pp. 1298 ff. is nice on the role of introspection (“verstehen”) and teleology in economics, and ordinalism as an attempt to get rid of that and turn economics into a mechanic science, with nice citations of Weber. P. 1299 footnote 7 defines teleology discussed jointly with psychological hedonism, which is close to utilitarianism. P. 1304: behaviorism was movement away from teleology, to turn sychology into a mechanical science like physics. (Also p. 1308 for ordinalism in economics.) P. 1305: psychological hedonism took utility as primitive, and it was not observable. She lets force (and energy similar, but mostly force) from physics (not a primitive concept but only derived from movements of bodies) have a role similar to utility. P. 1309, as so many, misunderstands Pareto (1901). He writes: “Let others concern themselves with the nature, with the essence of value. I am interested only in seeing whether I can discover which regularities are presented by prices (1901, p. 204).” So Pareto does not say there is anyhing wrong in inspecting value and essence, only that he now does not do so. However, Lewin will take him to say the former (which he did not say). P. 1310 announces difference between ordinalism and behaviorism but only discusses revealed preference of Samuelson which is a nice contribution but in which I see no difference. P. 1312: “Cardinal utility was more than a particular theoretical concept; it symbolized verstehen.” P. 1313: according to Knight, we cannot dispense with motives as we can dismiss with force in physics. P. 1315 and elsewhere (p.; 1317): “It was simply not empirically possible to base preference theory on behavior alone.” P. 1315 refers to several economic studies in the 1930s trying to measure utility empirically, such as Thurstone (1931). %} Lewin, Shira (1996) “Economics and Psychology: Lessons for Our Own Day from the Early Twenthieth Century,” Journal of Economic Literature 34, 1293–1323. {% Seems to find violations of RCLA %} Lewis, Barry L. & Jan Bell (1985) “Decisions Involving Sequential Events: Replications and Extensions,” Journal of Accounting Research 23, 228–239. {% discounting normative: seems to argue against discounting. %} Lewis, Clarence I. (1946) “An Analysis of Knowledge and Valuation.” Open Court, La Salle. {% %} Lewis, Charles & Gideon Keren (1999) “On the Difficulties Underlying Bayesian Reasoning: A Comment on Gigerenzer and Hoffrage,” Psychological Review 106, 411–416. {% conditional probability Principle of Complete Ignorance: seems to discuss this view that events that happen or not, cannot be assigned probabilities. %} Lewis, David (1980) “A Subjectivist’s Guide to Objective Chance.” In Richard C. Jeffrey (ed.) Studies in Inductive Logic and Probability, Vol. II, 263–293, University of California Press, Berkeley. Reprinted (with added postcripts) in David Lewis (1986) Philosophical Papers: Volume II, 83–132, Oxford University Press, New York. {% conditional probability; %} Lewis, David (1986) “Probabilities of Conditionals and Conditional Probabilities II,” Philosophical Review XCV, 581–589. {% %} Lewis, David (1987) “Causal Decision Theory,” In Peter Gärdenfors & Nils-Eric Sahlin (eds.) Decision, Probability, and Utility, 377–405, Cambridge University Press, Cambridge. {% %} Lewis, David (1986) “On the Plurality of Worlds.” Blackwell, Oxford. {% About friendship of Kahneman & Tversky. %} Lewis, Michael (2016) “The Undoing Project: A Friendship That Changed Our Minds.” W. W. Norton & Company, New York. {% foundations of quantum mechanics %} Lewis, Peter J. (2006) “Conspiracy Theories of Quantum Mechanics, “British Journal for the Philosophy of Science 57, 359–381. {% natural sources of ambiguity; Contributions: (1) First comprehensive measurement of ambiguity attitude, including insensitivity, in the developing world (p. 242 3rd para); (2) Introduces an important new source of ambiguity: linguistic ambiguity; (3) Studies wealth effects on ambiguity with big income difference (factor 10) but more similarities and fewer differences between the subjects otherwise than in other studies, made possible because of a typical local difference between mountain- and city inhabitants (p. 241-242 & p. 258 bottom); (4) contributes to literature showing importance of a(mbiguity-generated) insensitivity, capturing more variance than ambiguity aversion. Within the rural group the poorer are more ambiguity averse and a-insensitive (p. 251 2nd para). Within urban group, rich are more insensitive, may be because they are so rich that they can be lazy. For poor group, higher irrationality (my interpretation) of poor group can add to poverty trap. (p. 241 4/5). Between group, rural are more ambiguity averse and a-insensitive (p. 242 3rd para). P. 242: “a-insensitivity captures to what extent people understand the ambiguous decision situation from a cognitive perspective.” P. 258 middle reiterates it. P. 242 last para: “the clear classification of a-insensitivity as irrational … it is easier for people to learn about their cognitive mistakes than the emotional ones.” P. 243 middle: “But this symmetry [as in Ellsberg urn] does not hold in general for natural ambiguity sources.” P. 249 last line reports asymmetry found in data. P. 243: subjects (high school age 17) were given phrases in foreign languages of which three possible meanings were given (one correct), and sudents had to gamble money depending on the correct meaning. Every sentence was taken as a different source of uncertainty (p. 243). P. 244: difference between subjective probability and matching probability can be taken as an ambiguity premium. P. 246: RIS was used where each subject played one randomly chosen choice for real. P. 248/249: correlation between multiple switching and score on Fredricks’cognitive reflection. P. 249, on subjective probabilities in source method: “capture subjects’ subjective beliefs (although distorted by their ambiguity attitudes)” P. 256: rich urbans were ambiguity seeking. P. 257: discusses policy implications of insensitivity, not in the cliché way as in most papers, but nicely. %} Li, Chen (2017) “Are the Poor Worse at Dealing with Ambiguity? Ambiguity Attitude of Urban and Rural Chinese Adolescents,” Journal of Risk and Uncertainty 54, 239–268. {% DOI: http://dx.doi.org/10.1007/s11238-013-9375-2 paternalism/Humean-view-of-preference %} Li, Chen, Zhihua Li, & Peter P. Wakker (2014) “If Nudge Cannot Be Applied: A Litmus Test of the Readers’ Stance on Paternalism,” Theory and Decision 76, 297–315. Link to paper
Li, Chen, Uyanga Turmunkh, & Peter P. Wakker (2017) “Trust as a Decision under Ambiguity,” Experimental Economics, forthcoming. {% inverse-S: find pessimism iso inverse-S. This can, however, be explained by a confound. They asked, in Russian roulette, for the WTP and happiness for removing one bullet, with j bullets (1 j 6) present. Subjects did not just answer what the increase in happiness was as the authors assume, but what the happiness in the final situation is. %} Li, Li-Bo, Shu-Hong He, Shu Li, Jie-Hong Xu, & Li-Lin Rao (2009) “A Closer Look at the Russian Roulette Problem: A Re-Examination of the Nonlinearity of the Prospect Theory’s Decision Weight ,” International Journal of Approximate Reasoning 50, 515–520. {% %} Li, Hao & Wing Suen (2004) “Delegating Decisions to Experts,” Journal of Political Economy 112, 311–355. {% The index of riskiness of a lottery (only mixed) is the level of absolute risk aversion makinhg the lottery equivalent to 0. The author gives easy upper and lower bounds, he considers sums of lotteries that, unlike with Aumann, can also be not-independent, extends it to general (also nonmixed) lotteries relative to also nonzero prices, and he gives multiplicative analogs of the preceding additive results. The latter can be used to characterize decreasing or increasing relative risk aversion. %} Li, Minqiang (2014) “On Aumann and Serrano’s Economic Index of Risk,” Economic Theory 55, 415–437. {% Finds that if common outcome transparent, then not always cancellation %} Li, Shu (1994) “What is the Role of Transparency in Cancellation?,” Organizational Behavior and Human Decision Processes 60, 353–366. {% Says there is no preference reversal because (suggesting: “what nobody ever was aware of yet”) it is simply two different preference relations; %} Li, Shu (1994) “Is there a Problem with Preference Reversals?,” Psychological Reports 74, 675–679. {% too small amounts sometimes, ignoring curvature of utility, ad hoc cancellation-editing, etc. %} Li, Shu (1995) “Is there a Decision Weight ?,” Journal of Economic Behavior and Organization 27, 453–463. {% Studies situations where a principal receives sequential reports from agents. The agent may pretend to change mind more or less than appropriate to suggest more expertise, and the principal may desire to solicit sequential or one-time reporting depending on circumstances. %} Li, Wei (2007) “Changing One’s Mind when the Facts Change: Incentives of Experts and the Design of Reporting Protocols,” Review of Economic Studies 74, 1175–1194. {% Shows how prospect theory can accommodate a large number of financial phenomena. loss aversion: erroneously thinking it is reflection: not that confusion, but relatedly, the authors use the term diminishing sensitivity for what better be called reflection. %} Li, Yan & LiyanYang (2013) “Prospect Theory, the Disposition Effect, and Asset Prices,” Journal of Financial Economics 107, 715–739. {% PT, applications; Seem to review 10 empirical studies in transportation, finding that PT improves understanding. %} Li, Zheng & David A. Hensher (2011) ‘Prospect Theoretic Contributions in Understanding Traveller Behaviour: A Review and Some Comments’, Transport Reviews 31, 97–115. {% Players play a coordination game. For instance, they rank three metals, copper (E1), gold (E2), iron (E3) in places 1-3. A player is matched with a random opponent. If they ranked the same metal forst, they receive £20, and otherwise nothing. Next they must assess percentages of subjects with Ei for all I, and then Eij (= Ei Ej) (i j), that is probabilities, through probability equivalents, i.e., matching probabilities are measured (using BDM). The singleton matching probabilities add to more than 1 (would be 1 under Bayesianism; ambiguity seeking for unlikely), the composite to less than 2 (would be 2 under Bayesianism). This agrees with the common fourfold pattern of ambiguty attitude, although the overweighting of singletonsis greater than usual. They do and find the same for seven other triples, flowers, etc. They also measure other things, such as certainty equivalents, but do not use those in the analysis. %} Li, Zihua, Graham Loomes, & Ganna Pogrebna (2017) “Attitudes to Uncertainty in a Strategic Setting,” Economic Journal 127, 809–826. {% %} Li, Zhihua, Julia Müller, Peter P. Wakker, & Tong V. Wang (2017) “The Rich Domain of Ambiguity Explored,” Management Science, forthcoming. {% %} Li, Zhihua, Kirsten I.M. Rohde, & Peter P. Wakker (2017) “Improving One’s Choices by Putting Oneself in Others’ Shoes—An Experimental Analysis,” Journal of Risk and Uncertainty 54, 1–13. link to paper
Liao, Stephen, Tanying Wu, Raymond Lau, & Itadong Zhang (2011) “Coalition Formation Based on Marginal Contributions and the Markov Process,” {% Seems to show that every finite, vector-values, non-atomic, countably additive measure is closed and convex. %} Liapunov, Aleksandr Mikhailovich (1940) “Sur les Fonctions-Vecteurs Complètement Additives,” Izvestiya Akademii Nauk SSSR 5, 465–478. {% Reviews evidence that people, unassisted, lack the ability to behave as Bayesian statisticians. %} Libby, Robert (1981) “Accounting and Human Information Processing: Theory and Applications.” Prentice-Hall, Englewood Cliffs, NJ. {% Risk averse for gains, risk seeking for losses & utility concave near ruin: risk aversion near ruin: pp. 285-286 suggest that the probability of ruin plays a special role, also middle of p. 287, a point reiterated extensively on pp. 288-289. %} Libby, Robert & Peter C. Fishburn (1977) “Behavioral Models of Risk Taking in Business Decisions: A Survey and Evaluation,” Journal of Accounting Research 15, 272–292. {% Cognitive ability is related to probability judgments. The authors consider several numeracy/intelligence tests, and relate them to each other with factor analyses and relate them also to disjunction and conjuction and ratio-bias fallacies in probability judgement. The natural finding is that more intelligence leads to fewer biases. The authors point out that there hasn´t yet been much theory on the cognitive abilities relevant here, and contribute to that, giving more refined results. They identify some factors of intelligence and their effect on biases. %} Liberali, Jordana M., Valerie F. Reyna, Sarah Furlan, Lilian M. Stein, & Seth T. Pardo (2012) “Individual Differences in Numeracy and Cognitive Reflection, with Implications for Biases and Fallacies in Probability Judgment,” Journal of Behavioral Decision Making 25, 361–381. {% probability elicitation %} Liberman, Varda & Amos Tversky (1993) “On the Evaluation of Probability Judgments: Calibration, Resolution, and Monotonicity,” Psychological Bulletin 114, 162–173. {% free-will/determinism: famous experiment on free will. Asked subjects to move right hand at moment chosen themselves. Had to indicate exact point of decision. However, EEG-registered brain activities prepared the movement earlier, prior to, the indicated time of decision. So consciousness seems to come after decision in brains. This study initiated many similar studies. The conclusion about free will was contested by the Dutch psychologist Herman Kolk. He cited William James’ (1890) ideomotor theory and his famous example of getting up without a conscious decision to that effect: there are impulses pro and impulses con. Subjects are asked to push a button, giving impulses pro doing it, but are also asked not to do it immediately, which are impulses con ion the beginning. If may be the disappearance of the impulses con that generate the push of button, without there having been some decision pro. Such a quasi-decision is only stated later by the subject so as to ex post justify for himself or others what happened. %} Libet, Benjamin, Curtis A. Gleason, Elwood W. Wright & Dennis K. Pearl (1983) “Time of Conscious Intention to Act in Relation to Onset of Cerebral Activities (Readiness-Potential): The Unconscious Initiation of A Freely Voluntary Act,” Brain 106, 623–642. {% ordering of subsets %} Licalzi, Marco (1998) “Variations on the Measure Representation Approach,” Journal of Mathematical Economics 29, 255–269. {% utility families parametric; investigate the Pearson parametric family, proposed to fit probability distributions, for the purpose of a parametric utility family. One parameter, m, is the reference point, and then the family can be concave or convex below it, also above it, and can have any of four combinations. It extends the HARA family. For some parameters the maximum support is bounded. §6 briefly discusses the use for probability weighting. %} LiCalzi, Marco & Annamaria Sorato (2006) “The Pearson System of Utility Functions,” European Journal of Operational Research 172, 560–573. {% DOI: http://dx.doi.org/10.1287/mnsc.1120.1667 Show that, for expert aggregation, averaging quantiles usually works better than averaging probability estimates. %} Lichtendahl, Kenneth C. Jr., Yael Grushka-Cockayne, & Robert L. Winkler (2013) “Is It Better to Average Probabilities or Quantiles?,” Management Science 59, 1594–1611. {% probability elicitation: survey of calibration; survey on belief measurement: %} Lichtenstein, Sarah, Baruch Fischhoff, & Lawrence D. Phillips (1977) “Calibration of Probabilities: The State of the Art.” In Helmut Jungermann & Gerard de Zeeuw (eds.) Decision Making and Change in Human Affairs. Reidel, Dordrecht. {% probability elicitation: survey of calibration; find widespread overconfidence; a follow-up is in McClelland & Bolger (1994) %} Lichtenstein, Sarah, Baruch Fischhoff, & Lawrence D. Phillips (1982) “Calibration of Probabilities: The State of the Art to 1980.” In Daniel Kahneman, Paul Slovic, & Amos Tversky (eds.) Judgment under Uncertainty: Heuristics and Biases, Cambridge University Press, Cambridge. {% original reference %} Lichtenstein, Sarah & Paul Slovic (1971) “Reversals of Preference between Bids and Choices in Gambling Decisions,” Journal of Experimental Psychology 89, 46–55. {% gamblers in Las Vegas; using real stakes %} Lichtenstein, Sarah & Paul Slovic (1973) “Response-Induced Reversals of Preferences in Gambling: An Extended Replication in Las Vegas,” Journal of Experimental Psychology 101, 16–20. {% inverse-S: in !judgments of frequencies!, people exhibit inverse-S. %} Lichtenstein, Sarah, Paul Slovic, Baruch Fischhoff, Mark Layman, & Barbara Combs (1978) “Judged Frequency of Lethal Events,” Journal of Experimental Psychology: Human Learning and Memory 4, 551–578. {% Use graphs to represent strengths of preferences, with the length of an arrow indicating strength of preference. %} Lidouh, Karim, Yves De Smet, & Minh Tuan Huynh (2009) “Circular Representations of a Valued Preference Matrix,” Université Libre de Bruxelles. {% Find that duration neglect disappears when episodes are represented using graphs, rather than memory. Frankly, this is not surprising. %} Liersch, Michael J. & Craig R.M. McKenzie (2009) “Duration Neglect by Numbers—And Its Elimination by Graphs,” Organizational Behavior and Human Decision Processes 108, 303–314. {% inverse-S: moderately experienced poker players estimate probability of winning, given their cards, quite well, although they overestimate some probabilities below 0.7 and underestimate them above. This may be mere regression to the mean. %} Liley, James & Tim Rakow (2010) “Probability Estimation in Poker: A Qualified Success for Unaided Judgment,” Journal of Behavioral Decision Making 23, 496–526. {% Study + references on correcting intransitivities. %} Linares, Pedro (2009) “Are Inconsistent Decisions Better? An Experiment with Pairwise Comparisons,” European Journal of Operational Research 193, 492–498. {% Only reasonable way to determine social rate of discounting is by eliciting time preference of individuals. Gives examples where people do different trades with different discountings in different domains. People save at some interest rate but at the same time pay with credit cards with higher rates of interest. This is not only because of transaction costs but also because of a kind of mental accounting, e.g. to control certain kinds of spending differently than others. Thus, in particular, people need not be affected much by the market interest rate. %} Lind, Robert C. (1990) “Reassessing the Government’s Discount Rate Policy in the Light of New Theory and Data in a World Economy with a High Degree of Capital Mobility,” Journal of Environmental Economics and Management 18, S8–S28. {% If two persons are altruistic then that may generate inefficiencies, such as me deliberately consuming more the first period knowing that my partner (and me) out of altruistic reasons will share in the second period, and my altruism being present but smaller than my selfishness. %} Lindbeck, Assar & Jörgen Weibull (1988) “Altruism and Time Consistency: The Economics of Fait Accompli,” Journal of Political Economy 96, 1165–1182. {% Measure risk aversion, simply via # times of preference for smaller variance, whilst it is specified what fixed outcome a nonanonymous opponent gets. The latter was an opponent before in a Bertrand game (where both choose price and the lowest price gets the whole market, so very competitive). When the opponent’s outcome is above the lottery outcomes, there is (just) more risk aversion than when not. If the opponent’s outcome serves as a reference point, this finding goes against the less risk aversion for losses that prospect theory posits. I am interested in speculations on the emotions that the prior Bertrand game may have generated to explain this. P. 51 speculates that, because utility is steeper for losses, there will be fewer errors for losses. Although early studies suggested more errors for losses, several studies by Eldad Yechiam, e.g. Yechiam, Retzer, Telpaz, & Hochman (2015) confirmed fewer errors, showing that with losses involved subjects pay more attention. Download 7.23 Mb. Share with your friends:
|