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| §7 then analyses a well-known decision example used for illustration in the decision analysis literature: the Carter racing case study. I must admit that I did not understand part of the notation here, apparently not having read the paper in sufficient detail. The discussion section 7.1 is more positive about ambiguity than I Bayesian could be. The end of the discussion properly mentions that a dynamic implementation of nonEU is nontrivial. I think that no nonEU model will survive any dynamic implementation. The strongest arguments in favor of Bayesianism come from dynamic consistency type conditions, the violation of which no rational decision maker should desire. %}
Borgonovo, Emanuele & Massimo Marinacci (2015) “Decision Analysis under Ambiguity,” European Journal of Operational Research 244, 823–836. {% Shows that incomplete preference relation over lotteries satisfying independence can be extended to a complete one. Gives a lexicographic representation. %} Borie, Dino (2016) “Lexicographic Expected Utility without Completeness,” Theory and Decision 81, 167–176. {% Do simulation to see effects of publication bias. This study could appear in any academic journal. %} Borm, George F., Martin den Heijer, & Gerhard A. Zielhuis (2009) “Publication Bias Was not a Good Reason to Discourage Trials with Low Power,” Journal of Clinical Epidemiology 62, 47–53. {% %} Bornemann, Ernest (1976) “The Psychoanalysis of Money.” Urizen Books, New York. {% %} Bosch, Johanna L. & Maria G.M. Hunink (1996) “The Relationship between Descriptive and Valuational Quality-of-Life Measures in Patients with Intermittent Claudication,” Medical Decision Making 16, 217–225. {% losses from prior endowment mechanism: did this, but very carefully, where 32 subjects received a prior endowment and then had to return 3 months later, giving them as much chance as possible to integrate the prior endowment into their reference point. 30 subjects indeed returned to undergo the losses from their prior endowment. Nice again, they asked about subjects’ perception. About 25% or 30% suggested that they do not consider the later losses as losses because they integrate with the prior endowment. The data were not very good for prospect theory, but I forgot details now in August 2006 (about month after hearing lecture). %} Bosch-Domènech, Antoni & Joaquim Silvestre (2010) “Averting Risk in the Face of Large Losses: Bernoulli vs. Tversky and Kahneman,” Economics Letters 107, 180–182. {% Consider a bias in the Holt Laury (2002) risk aversion measurement that results from adding/removing some options. The method of Abdellaoui, Driouchi, & l’Haridon (2011) is found not to be subject to such biases. %} Bosch-Domènech, Antoni & Joaquim Silvestre (2013) “Measuring Risk Aversion with Lists: A New Bias,” Theory and Decision 75, 465–496. {% %} Bosch, Johanna L., James K. Hammitt, Milton C. Weinstein, & Maria G.M. Hunink (1998) “Estimating General-Population Utilities Using One Binary Gamble Question per Respondent,” Medical Decision Making 18, 381–390. {% risk seeking for symmetric fifty-fifty gambles: they don’t have fifty-fifty gambles, but do find risk seeking for small amounts. PT falsified. Consider gains and losses, and probabilites 0.20 and 0.80 of getting the gain or loss. Compare $800.2$0 and $800.2$0). Can be done in two steps: step 1, translation by subtracting $80, so that $800.2$0 is changed into $00.2$80. Step 2, switching good- and bad-outcome probability, so that $00.2$80 is changed into $00.8$80. They find that translation from gains to losses always increases risk seeking, both for high-probability and for low-probability for best outcome. They find that switching probability of bad outcome from 0.2 to 0.8 always increases risk seeking, both for gains and for losses. Testing reflection for high-probability nonzero has translation and switch go in same direction, enhancing risk seeking for losses. Testing reflection for low-probability nonzero has translation and swiches go in opposite directions. In prospect theory, probability weighting and utility curvature have opposite effects for small-probability-nonzero-outcomes, although they both support the reflection effect because they both switch from gains to losses. Consider also 7 different stakes. People are risk averse for high stakes and risk seeking for small, for high and low probabilities and for gains and losses (probability weighting depends on outcomes). Maybe some Utility of gambling generating the risk seeking for small amounts!? So that we may want to avoid small-amount prospects, considering this just a bias? %} Bosch-Domènech, Antoni & Joaquim Silvestre (2006) “Reflections on Gains and Losses: A 227 Experiment,” Journal of Risk and Uncertainty 33, 217–235. {% Wealthy are more risk seeking at low stakes but, strangely enough, the poor at high stakes. %} Bosch-Domènech, Antoni & Joaquim Silvestre (2005) “Do the Wealthy Risk More Money? An Experimental Comparison,” CREA, University Pompeu Fabra, Barcelona. {% Auctions with ambiguity aversion ( contamination) give different results than under EU. %} Bose, Subir & Arup Daripa (2009) “A Dynamic Mechanism and Surplus Extraction under Ambiguity,” Journal of Economic Theory 144, 2084–2114. {% Assume a finite number of observations from budget sets that contain event-contingent payoffs (acts). Give necessary and sufficient conditions for these choices to maximize maxmin EU (multiple priors) or the smooth model. The conditions given are not directly in terms of preferences, but instead require existence of sets of probabilities, utilities, and so on, such that their necessary and sufficient condition is satisfied. %} Bose, Subir, Matthew Polissony, & Ludovic Renou (2012) “Ambiguity Revealed.” {% Introduces ambiguity averse (maxmin EU) agents into mechanism design. %} Bose, Subir & Ludovic Renou (2014) “Mechanism Design with Ambiguous Communication Devices,” Econometrica 82, 1853–1872. {% %} Bosi, Gianni (1995) “Linear Representations of Preference Relations on a Mixture Set,” Trieste, Italy. {% %} Bosi, Gianni & Gerhard Herden (2012) “Continuous Multi-Utility Representations of Preorders,” Journal of Mathematical Economics 48, 212–218. {% %} Bosi, Gianni & Romano Isler (1995) “Representing Preferences with Nontransitive Indifference by a Single Real-Valued Function,” Journal of Mathematical Economics 24, 621–631. {% Their global risk idea, not finding all the same results as before; now also measuring emotions and relating them to observed behavior. %} Bosman, Ronald & Frans van Winden (2010) “Global Risk, Investment and Emotions,” Economica 77, 451–471. {% %} Bosmans, Kristof (2007) “Comparing Degrees of Inequality Aversion,” Social Choice and Welfare 29, 405–428. {% Ambiguity in market. Heterogeneity in ambiguity attitude has extra inertia effects of neither buying nor selling ambiguous option for wider ranges of prices, which is something different than heterogeneity in risk attitude. Some qualitative theoretical predictions about agents being more certainty-seeking under ambiguity than any smooth model could explain, with bid-ask spread, are confirmed in experiments. correlation risk & ambiguity attitude: find positive correlation between risk aversion and ambiguity aversion. They use -maxmin model. The authors assume, in 3-color urn, that red has weight 1/3, and for black they assume a set of possible probabilities [a,b]. It was not clear to me if a and b are exogenous or endogenous. The theoretical part does not say, in the experiment it seemed to be endogenous (or was it [0, 2/3]?). But then they influence ambiguity aversion and interact with . They find support for nonsmooth ambiguity attitudes as opposed to the smooth KMM model (e.g. p. 1329 3rd para). They paid subjects repeatedly, so that income effects could arise. They do several drawings from the same unknown urn without replacement. Bayesian rational subjects, hence, will be ambiguity seeking in the sense of rather playing the unknown urn! I will rather gamble on the unknown color that occurred most often so far than on the known color. %} Bossaerts, Peter, Serena Guarnaschelli, Paolo Ghirardato, & William Zame (2010) “Ambiguity and Asset Prices: An Experimental Perspective,” Review of Financial Studies 23, 1325–1359. {% Refers to Peters & Wakker (1992) %} Bossert, Walter (1994) “Rational Choice and Two-Person Bargaining Solutions,” Journal of Mathematical Economics 23, 549–563. {% ordering of subsets %} Bossert, Walter (1996) “Uncertainty Aversion in Nonprobabilistic Decision Models,” Mathematical Social Sciences 34, 191–203. {% Nash bargaining solution %} Bossert, Walter, Ed Nosal, & Venkatraman Sadanand (1996) “Bargaining under Uncertainty and the Monotone Path Solutions,” Games and Economic Behavior 14, 173–189. {% Single-basined means that there can be multiple worst alternatives. Consider as choice domain all compact convex subsets of n. Assume IIA, and derive representation. Corollary 2 shows that the choice function is representable by a weak order. (They show transitivity there, but completeness can then be obtained.) %} Bossert, Walter & Hans J.M. Peters (2014) “Single-Basined Choice,” Journal of Mathematical Economics 52, 162–168. {% Define a choice function usual way, assigning to each subset of set of alternative an element. If I understand right, for each choice function they can associate with each subset of alternatives a game in extensive form with perfect information having those alternatives as possible outcomes and the chosen element as the solution of backward induction. Exact restrictions of domains here I did not study enough. %} Bossert, Walter & Yves Sprumont (2013) “Every Choice Function Is Backwards-Induction Rationalizable,” Econometrica 81, 2521–2543. {% Show for welfare evaluations that all relations satisfying the transfer principle (something like elementary mean-preserving spread) and Pareto optimality and anonymity are extensions of a Suppes relation, which is the most elementary transitive extension of Pareto optimality and the transfer principle. %} Bossert, Walter, Yves Sprumont, & Kotaro Suzumura (2007) “Ordering Infinite Utility Streams,” Journal of Economic Theory 135, 579–589. {% time preference: %} Bossert, Walter & Frank Stehling (1992) “A Remark on Admissible Transformations for Interpersonally Comparable Utilities,” International Economic Review 33, 739–744. {% revealed preference: variations on Richter's (1966) consistency condition, with and without reflexivity/completeness and if domain does not contain all two-point subsets. %} Bossert, Walter & Kotaro Suzumura (2005) “Consistent Rationalizability,” Economica 72, 185–200. {% Theorem 2, p. 716, characterizes an incomplete and intransitive EU representation, with a best outcome M and a worst outcome m, and: M m; x ~ Mpm with p the EU of lottery x (so we can use the standard gamble method). x y EU(x) > EU(y) x ~ y EU(x) = EU(y) Necessary and sufficient preference conditions: Suzumura consistency, solvability, monotonicity and independence. %} Bossert, Walter & Kotaro Suzumura (2015) “Expected Utility without Full Transitivity,” Social Choice and Welfare 45, 707–722. {% foundations of probability: nineteenth century debates of physicians on use/meaning of probability %} Bossuyt, Patrick M.M. (1997) “De Idolen van Kieslowski,” inaugurale rede, University of Amsterdam, the Netherlands. {% bisection > matching; Many references on preference reversal; find that ping-pong method of elicitation greatly reduces Choice vs. Pricing preference reversals. Judged CEs (certainty equivalents) and choice-based CEs can differ substantially for some gambles. %} Bostic, Raphael, Richard J. Herrnstein, & R. Duncan Luce (1990) “The Effect on the Preference-Reversal Phenomenon of Using Choice Indifferences,” Journal of Economic Behavior and Organization 13, 193–212. {% PT, applications: Analyze risks due to flooding in the Netherlands, with special interest in changing climate. Use prospect theory and RDU to calculate risk premiums. See if there is space for insurance. %} Botzen, Wouter J.W. & Jeroen C.J.M. van den Bergh (2008) “Insurance Against Climate Change and Flooding in the Netherlands: Present, Future, and Comparison with Other Countries,” Risk Analysis 28, 413–426. {% small probabilities: the authors ask not only for assessment of the likelihoods of extreme events, but also of the damage resulting. They claim this joint assessment as their novelty. inverse-S: they confirm overestimation of small probabilities. The extreme damages are underestimated. %} Botzen, Wouter W. J., Howard Kunreuther, & Erwann Michel-Kerjan (2015) “Divergence between Individual Perceptions and Objective Indicators of Tail Risks: Evidence from Floodplain Residents in New York City,” Judgment and Decision Making 10, 365–385. {% The authors did an experiment in Ethiopia with students. Measured CEs of binary prospects for risk and ambiguity (the latter with unknown urns), for all probabilities j/8. Could have incentives like weekly income. They study stake effects, by considering baseline stakes and then those doubled. They do it between-subjects. All their findings mean that likelihood insensitivity increases for high stakes, both for risk and ambiguity (latter is difference between uncertainty and risk). A difficulty is that their measure of aversion is derived from a regression line through certainty equivalents, normalized by dividing by the largest (absolute value of) outcome. The measure makes it hard to see if it is relative or absolute or yet something different, and it is affected by both utility and probability weighting. Thus stake-dependence may just refer to utility. Futher, if utility is differentiable, then utility tends to linear if variance (so, here, stake seize) tends to 0, so a normalized risk premium tends to neutrality, which numerical effect alone may determine stakes effect. In particular, it does not speak to: probability weighting depends on outcomes. %} Bouchouicha, Ranoua, Peter Martinsson, Haileselassie Medhin, & Ferdinand M. Vieider (2017) “Stake Effects on Ambiguity Attitudes for Gains and Losses,” Theory and Decision 83, 19–35. {% Find risk seeking for small outcomes but risk aversion for large ones. A generalized logarithmic utility (ln (x + a)) fits better than the common log-power or linear-exponential. The authors use hypothetical choices for losses and so as to examine real large stakes. They also find some violations of separability of probability weighting versus utility of outcome. (PT falsified; probability weighting depends on outcomes). decreasing ARA/increasing RRA: they find increasing relative risk aversion! %} Bouchouicha, Ranoua & Ferdinand M. Vieider (2017) “Accommodating Stake Effects under Prospect Theory,” Journal of Risk and Uncertainty 55, 1–28. {% %} Bourbaki, Nicolas (1971) “Eléments de Mathématiques, Topologie Générale.” Diffusion CCLS, Paris. {% doi: http://dx.doi.org/10.1098/rstb.2009.0163 Neuro-studies seem to find regret in the brains. The author suggests that this gives a normative basis to regret theory. %} Bourgeois-Gironde, Sacha (2010) “Regret and the Rationality of Choices,” Philosophical Transactions of the Royal Society B 365, 249–257. {% %} Boutilier, Craig (2002) “A POMDP Formulation of Preference Elicitation Problems,” Dept. of Cumputer Science, University of Toronto, Toronto, Canada. {% %} Boutilier, Craig, Nir Friedman, & Joseph Y. Halpern (2002) “Belief Revision with Unreliable Observations,” Dept. of Cumputer Science, University of Toronto, Toronto, Canada. {% %} Bouyssou, Denis (2005) “Conjoint Measurement Tools for MCDM; A Brief Introduction.” In José Figueira, Salvatore Greco & Matthias Ehrgott (2003, eds.) State of the Art of Multiple Criteria Decision Analysis, 73–132, Springer, Berlin. {% %} Bouyssou, Denis, Didier Dubois, Henri Prade, & Marc Pilot (2006) “Decision-Making Process: Concepts and Methods.” Wiley, New York. {% Additive conjoint measurement when there are only a finite nr. of categories that the n-tuples can belong to. %} Bouyssou, Denis & Thierry Marchant (2009) “Ordered Categories and Additive Conjoint Measurement on Connected Sets,” Journal of Mathematical Psychology 53, 92–105. {% Paper assumes that we only observe whether acts are better or worse than a status quo. It shows that the tradeoff consistency condition (Tradeoff method) then still gives expected utility. This approach with incomplete preference is in the spirit of works by Karl Vind and by Han Bleichrdt (2009, JMP). %} Bouyssou, Denis & Thierry Marchant (2011) “Subjective Expected Utility without Preferences?,” Journal of Mathematical Psychology 55, 457–468. {% %} Bouyssou, Denis, Thierry Marchant, Marc Pirlot, Alexis Tsoukias, & Philippe Vincke (2006) “Evaluation and Decision Models with Multiple Criteria.” Springer, Berlin. {% cancellation axioms: examines cancellation axioms without transitivity. The results are also of interest to readers interested only in transitive relations, because these general models nicely illustrate the meaning of all kinds of preference conditions. For instance, Table 1 on p. 683 nicely illustrates how triple cancellation and tradeoff consistency axioms amount to separability of pairs (xi,yi) in preferences (x1, ..., xn) (y1, ..., yn), and how separability amounts to similar separability only of pairs (xi,xi) (“void influence”). Triple cancellation: ziai wibi and zici widi & xiai yibi imply xici yidi RC1 on p. 686: not ziai wibi and zici widi and xiai yibi imply xici yidi RC2 on p. 686 (with change of symbols): ziai wibi and zici widi and not xiai yibi imply xici yidi They are the kinds of weakenings called independence by Karl Vind. %} Bouyssou, Denis & Marc Pirlot (2003) “Nontransitive Decomposable Conjoint Measurement,” Journal of Mathematical Psychology 46, 677–703. {% standard-sequence invariance; Tradeoff method %} Bouyssou, Denis & Marc Pirlot (2004) “A Note on Wakker’s Cardinal Coordinate Independence,” Mathematical Social Sciences 48, 11–22. {% Intransitivity in multi-attribute. %} Bouyssou, Denis & Marc Pirlot (2004) “Preferences for Multi-Attributed Alternatives: Traces, Dominance, and Numerical Representations,” Journal of Mathematical Psychology 48, 167–185. {% %} Bouyssou, Denis & Marc Pirlot (2004) “Additive Difference’ Models without Additivity and Subtractivity,” Journal of Mathematical Psychology 48, 263–291. {% Tradeoff method: use it to obtain a joint generalization of expected utility and the likely dominance model (choice the alternative that on more than half of the state space (measured in terms of subjective probability) dominates the other). Show that in terms of comparing tradeoffs, the latter model is very crude in only considering the sign of the tradeoff. %} Bouyssou, Denis & Marc Pirlot (2008) “On Some Ordinal Models for Decision Making under Uncertainty,” Annals of Operations Research 163, 19–48. {% risky utility u = strength of preference v (or other riskless cardinal utility, often called value) if normative; maybe also descriptive. %} Bouyssou, Denis & Jean-Claude Vansnick (1988) “A Note on the Relationships between Utility and Value Functions.” In Bertrand R. Munier (ed.) Risk, Decision and Rationality, 103–114, Reidel, Dordrecht. {% %} Bouyssou, Denis & Jean-Claude Vansnick (1990) “Utilité Cardinale dans le Certain 5et Choix dans le Risque,” Revue Économique 41, 979–1000. {% Use Tradeoff method %} Bouzit, A. Madjid & Guy Gleyses (1996) “Empirical Estimation of RDEU Preference Functional in Agricultural Production,” GRID, ENS, Cachan, France. {% text of inugurale redeof 15Dec2016. own little expertise = meaning of life: P. 6 footnote 2: “dat het er in het programma niet in de eerste plaats om gaat dat de scholier de economiepagina in de krant begrijpt maar ook zijn of haar eigen leven.” P. 12 “Hoe meer mensen verschillen in voorkeuren of talenten, hoe groter de potentiële meerwaarde van samenwerken.” P. 15 l. 1: “Toen de mens de kracht van werderzijds voordeel ontdekte, explodeerde de welvaart.” P. 15 l. 3: “Adam Smith—doorgrondde de grote betekenis van de balans win-win.” Then writes that besides win-lose and lose-win there is a third road, being win-win. P. 16: “Landen waar de overheid en de economie in dienst staan van een kleine elite zijn arm.” Has suggested before that this concerns, besides North Korea, also East Germany before unification with West Germany. P. 21: “ `Nobody ever saw a dog make a fair and deliberate exchange of one bone for another with another dog.’ … De mens heeft de wereld veroverd vanwege zijn verstand (deliberate and moraliteit (fair).” [italics from original]. The italics are a citation from Adam Smith, who therefore shares in this idea that animals know no (“delibrerate”) collaboration or exchange. %} Bovenberg, Lans (2016) “Economieonderwijs in Balans: Kiezen en Samenwerken.’ {% foundations of probability %} Bovens, Luc & Stephan Hartmann (2003) “Bayesian Epistemology.” Oxford University Press, New York. {% one-dimensional utility %} Bowen, Robert (1968) “A New Proof of a Theorem in Utility Theory,” International Economic Review 9, 374. {% %} Bowman, David, Deborah Minehart, & Matthew Rabin (1999) “Loss Aversion in a Consumption-Savings Model,” Journal of Economic Behavior and Organization 38, 155–178. {% P. 424: “Essentially, all models are wrong, but some are useful.” %} Box, George E. P. & Norman R. Draper (1987) Empirical Model Building and Response Surfaces, John Wiley & Sons, New York, NY. {% Assumes the repeated two-stage recursive utility form à la Koopmans. Proves existence and continuity of optima under proper assumptions. %} Boyd, John H. (1990) “Recursive Utility and the Ramsey Problem,” Journal of Economic Theory 50, 326–345. {% P. 59: “Category rating scales are subject to the same inconsistencies as the standard gamble.” Use SGs, VAS, and treatment choice; value colostomy for carcinoma of the rectum; five groups of, roughly, 35 participants each (patients with colostomy, physicians, two groups of healthy volunteers, and patients treated with radiotherapy but with no colostomy). Patients with colostomy valued it highest on SG and VAS, and were close second next to physicians in treatment choice. P. 66: “Thus, patients may regard a particular outcome of treatment as highly undesirable but then become accustomed to it when it is directly experienced, and learn to tolerate it well.” %} Boyd, Norman F., Heather J. Sutherland, Karen Z. Heasman, David L.Tritchler, Bernard J. Cummings (1990) “Whose Utilities for Decision Analysis”?, Medical Decision Making 10, 58–67. {% %} Bozbay, Irem, Franz Dietrich, Hans Peters (2014) “Judgment Aggregation in Search for the Truth,” Games and Economic Behavior 87, 571–590. {% %} Brachinger, Hans-Wolfgang & Martin Weber (1997) “Risk as a Primitive: A Survey of Measures of Perceived Risk,” OR-Spektrum 19, 235–260. {% Use quasi-hyperbolic discounting (-) to predict real-life choices. %} Bradford, David, Charles Courtemanche, Garth Heutel, Patrick McAlvanah, & Christopher Ruhm (2017), “Time Preferences and Consumer Behavior,” Journal of Risk and Uncertainty 55, 119–149. {% %} Bradley, Darren (2015) “Everettian Confirmation and Sleeping Beauty: Reply to Wilson,” British Journal for the Philosophy of Science 66, 683–693. {% By considering choice of gamble stake, favorite long-shot bias can be reconciled with prospect theory, but also with risk seeking for gains and risk aversion for losses. %} Bradley, Ian (2003) “The Representative Bettor, Bet Size, and Prospect Theory,” Economic Letters 78, 409–413. {% Provides a detailed discussion and clarification of Ramsey’s theorem. %} Bradley, Richard (2004) “Ramsey’s Representation Theorem,” Dialectica 58, 4: 483–497. {% R.C. Jeffrey model: shows that utilitarian aggregation is possible only if agents have same probability distribution. %} Bradley, Richard (2005) “Bayesian Utilitarianism and Probability Homogeneity,” Social Choice and Welfare 24, 221–253. {% R.C. Jeffrey model: unifies Savage etc. unconditional versus Jeffrey etc. conditional, referring to the work of KLST 71 and others. Much logic in the paper. %} Bradley, Richard (2007) “A Unified Bayesian Decision Theory,” Theory and Decision 63, 233–263. {% Argues that subjects can assign values to probabilities as they do to outcomes. This is a different interpretation than probability weighting. Mathematical differences remain to be investigated. %} Bradley, Richard (2016) “Ellsberg's Paradox and the Value of Chances,” Economics and Philosophy 32, 231–248. {% Consider the way climate change organizations report their uncertainty, including uncertainty about probabilities, and then propose ways to make normative decisions based on that. %} Bradley, Richard, Casey Helgeson, & Brian Hill (2017) “Climate Change Assessments: Confidence, Probability and Decision,” Philosophy of Science, forthcoming. {% %} Bradley, Richard & Christian List (2009) “Desire-as-Belief Revisited,” Analysis 69, 31–37. {% Counterfactuals impacting desirability sounds like violation of consequentialism. %} Bradley, Richard & H. Orii Stefansson (2015) “Counterfactual Desirability,” British Journal for the Philosophy of Science 68, 485-533. {% Dilation means that a state of objective probabilities can turn into imprecise info if new info is received. Although there is noting surprising about this, it is a paradox to those who erroneously think that a state of objective probability always reflects more info than states of ambiguity. Many in modern theories of ambiguity make the latter mistake implicitly. The authors discuss the cases with examples, references, and so on. %} Bradley, Seamus & Katie Steele (2012) “Uncertainty, Learning, and the “Problem” of Dilation,” working paper. {% %} Bradley, W. James, Jonathan K. Hodge, & D. Mark Kilgour (2005) “Separable Discrete Preferences,” Mathematical Social Sciences 49, 335–353. {% %} Braga, Jacinto, Steven J. Humphrey, & Chris Starmer (2009) “Market Experience Eliminates some Anomalies—and Creates New Ones,” European Economic Review 53, 401–416. {% Discuss Plott’s discovered preference hypothesis and suggest that it cannot explain all anomalies. %} Braga, Jacinto & Chris Starmer (2005) “Preference Anomalies, Preference Elicitation, and the Discovered Preference Hypothesis,” Environmental and Resource Economics 32, 55–89. {% %} Brams, Steven J. & Peter C. Fishburn (1992) “Coalition Voting,” Math. Comput. Modelling 16, 15–26. {% gender differences in risk attitudes: women more risk averse than men. %} Branas Garza, P. & Aldo Rustichini (2011) “Organizing Effects of Testosterone and Economic Behavior: Not Just Risk Taking,” PLoS ONE 6, e29842+. {% Games with incomplete information, Bayesian Rationality %} Brandenburger, Adam (1996) “Strategic and Structural Uncertainty in Games.” In Richard J. Zeckhauser, Ralph L. Keeney, & James K. Sibenius (1998) “Wise Choices: Games, Decisions, and Negotiations, 221–232. Harvard Business School Press, Boston. {% Games with incomplete information, Bayesian Rationality %} Brandenburger, Adam & Eddie Dekel (1986) “Subjective Expected Utility, Admissibility, and Posterior Rationality,” Department of Economics, University of California at Berkeley. {% Games with incomplete information, Bayesian Rationality %} Brandenburger, Adam & Eddie Dekel (1986) “Bayesian Rationality in Games,” Dept. of Applied Economics, Cambridge, MA. {% common knowledge; Seems to be readable version of Mertens & Zamir (1985) %} Brandenburger, Adam & Eddie Dekel (1993) “Hierarchies of Beliefs and Common Knowledge,” Journal of Economic Theory 59, 189–198. {% The priority heuristic works as follows: for gains: (1) Compare worst outcomes. If their difference is more than 1/10 times the best outcome, go for best worst outcome. (So no probabilities inspected apart from them being nonzero. Ratio-scale structure.) (2) If (1) did not decide, consider probabilities of worst outcomes. If they differ by more than 1/10, go for minimal probability. (3) If (2) did not decide either, consider best outcomes. For two-outcome prospects, now the best outcome decides (by point (2) their probabilities do not differ by much). For three- or more outcome prospects, if the difference of the best outcomes is more than 1/10 times the best outcome, go by the best one here. (4) If (3) did not work (then prospects have more than two outcomes), if probabilities of best outcomes differ by more than 1/10, go by them. (5) If not (4), then I guess indecision. For losses things are reflected. So we start with inspecting the best losses, and so on. There is only one example with mixed prospect, suggesting a bit that then the treatment is as with gains and with signs ignored, but it is not clear to me. %} Brandstätter, Eduard, Gerd Gigerenzer, & Ralph Hertwig (2006) “The Priority Heuristic: Making Choices without Trade-offs,” Psychological Review 113, 409–432. {% %} Brandstätter, Eduard, Gerd Gigerenzer, & Ralph Hertwig (2008) “Risky Choice with Heuristics: Reply to Birnbaum (2008) Johnson, Schulte-Mecklenbeck, and Willemsen (2008), and Rieger and Wang (2008),” Psychological Review 115, 281–289. {% %} Brandstätter, Eduard, Gerd Gigerenzer, & Ralph Hertwig (2008) “Postscript: Rejoinder to Johnson et al. (2008) and Birnbaum (2008),” Psychological Review 115, 289–290. {% utility depends on probability inverse-S: confirm it. In exp. 3 elicited certainty equivalents for some gambles (hypothetical only) using ping-pong à la Tversky & Fox (1995), only for one nonzero outcome. Assume that utility is x0.88 and then find inverse-S w confirmed. Do not say whether or not they used real incentives. They propose that inverse-S, overweighting of extreme outcomes, may be due to the surprise (elation if positive, disappointment if negative) that you feel about them if they are unlikely, and the extra utility or disutility that that surprise gives. So utility depends on probabilities. They ask people how surprised they feel if some low-probability outcome occurs, and like that grade the degree of surprise. Propose a formula that derives inverse-S from the degree of surprise (inverse-S (= likelihood insensitivity) related to emotions; cognitive ability related to risk/ambiguity aversion: ). They don’t link data on surprise to data on probability weighting. They don’t consider whether subjects with more surprise have more extreme inverse-S. The basic idea, that inverse-S is not through probability-perception per se, but through utility, is interesting. %} Brandstätter, Eduard, Anton Kühberger, & Friedrich Schneider (2002) “A Cognitive-Emotional Account of the Shape of the Probability Weighting Function,” Journal of Behavioral Decision Making 15, 79–100. {% Subjects do speak-aloud. Subjects do more between-gamble examinations (as formalized in tradeoffs) than within. %} Brandstätter, Eduard & Manuela Gussmack (2013) “The Cognitive Processes Underlying Risky Choice,” Journal of Behavioral Decision Making 26, 185–197. {% %} Brandstätter, Herman (1991) “Emotions in Everyday Life Situations: Time Sampling of Subjective Experience.” In Fritz Strack, Michael Argyle, & Norbert Schwarz (eds.) Subjective Well-Being: An Interdisciplinary Perspective, 173–192, Pergamon, Oxford. {% %} Brauers, Jutta & Martin Weber (1988) “A New Method for Scenario Analysis in Strategic Planning,” Journal of Forecasting 7, 31–47. {% %} Braun, Michael & Alexander Muermann (2004) “The Impact of Regret on the Demand for Insurance,” Journal of Risk and Insurance 71, 737–767. {% Seem to have written on the comparison of decision analysis advice with actual decisions. %} Brazier, John & Mark Deverill (1999) “A Checklist for Judging Preference-Based Measures of Health Related Quality of Life: Learning from Psychometrics,” Health Economics 8, 41–52. {% Test intertemporal separability and find it violated, though not to a very pronounced degree %} Brazier, John, Paul Dolan, Korina Karampela, & Isabel Towers (2006) “Does the Whole Equal the Sum of the Parts? Patient-Assigned Utility Scores for IBS-Related Health States and Profiles,” Health Economics 15, 543–551. {% Argues for the use of machine-learing techniques to replace many statistical modeling techniques. The author first did research, then consultancy, and then went back to research. For predictions from large data sets machine learning works better than statistical modeling techniques. P. 202 §5 1st para, on data models: “This enterprise has at its heart the belief that a statistician, by imagination and by looking at the data, can invent a reasonably good parametric class of models for a complex mechanism devised by nature.” P. 204: The author several times points out that statisticians uncritically start from some common modeling assumption, e.g. multivariate normal distribution of regressions, that will usually not hold: “Nobody really believes that multivariate data is multivariate normal, but that model occupies a large number of pages in every graduate textbook on multivariate statistical analysis. P. 205 2nd para points out that machine learning etc. usually does assume iid drawings. The author gives many examples where optimal fits in classical statistics and elsewhere have many local optima almost equally good but far apart, with minor changes in the data completely changing the solutions. P. 210 displays a claim: “The goal is not interpretation, but accurate information.” This may be true if in an application all one wants is good predictions there, but is not true in academic studies where one wants interpretations connecting with other fields and studies to acquire general knowledge. %} Breiman, Leo (2001) “Statistical Modeling: The Two Cultures,” Statistical Science 16, 199–215. {% %} Breiman, Leo & Don Freeman (1983) “How Many Variables Should be Entered in a Regression Equation,” Journal of the American Statistical Association 78, 131–136. {% Neural responses were monitored for monetary gambles, prior and posterior to deciding and learning about outcomes. P. 620, 2nd col. defines loss aversion as U(x) > U(x). They did repeated gambles with real payments and, hence, there was an income effect (p. 626). P. 627, top of 2nd col.: “The predominant responses to gains or their prospects were noted in the right hemisphere, whereas left hemisphere activations predominated in response to negative prospects.” There had been prior endowment to guarantee that no overal net loss results (prior endowment mechanism). P. 627 2nd column mentions that this may have reduced loss aversion effects. %} Breiter, Hans C., Ithak Aharon, Daniel Kahneman, Anders Dale, & Peter Shizgal (2001) “Functional Imaging of Neural Responses to Expectancy and Experience of Monetary Gains and Losses,” Neuron 30, 619–639. {% %} Bremmer, David & Peter P. Wakker (2008) “Verzeker vooral niet Alles,” Algemeen Dagblad, September 26, 2008. Link to paper
Brendl, C. Miguel (2000) “Subjective Experience and theEffect of Sample Size on Likelihood Judgments.” In Herbert Bless & Joseph P. Forgas (2000, eds.) The Message Within: The Role of Subjective Experience in Social Cognition and Behavior, 69–87, Psychology Press, Philadelphia. {% Criticizes, a.o., way in which the authors assume a true underlying model. Wallsten, Erev, & Budescu (2001) reply to it. %} Brenner, Lyle (2000) “Should Observed Overconfidence Be Dismissed as a Statistical Artifact? Critique of Erev, Wallsten, and Budescu (1994),” Psychological Review 107, 943–946. {% Consider pricing and direct probability judgments. Work along the lines of prospect theory, but bring in psychological processes of how likelihood judgments are derived from case-based reasoning (not related to the Gilboa-Schmeidler theory). %} Brenner, Lyle A., Dale W. Griffin, & Derek J. Koehler (2012) “A Case-Based Model of Probability and Pricing Judgments: Biases in Buying and Selling Uncertainty,” Management Science 58, 159–178. {% Seem to derive and confirm a number of implications of support theory. %} Brenner, Lyle & Derek J. Koehler (1999) “Subjective Probability of Disjunctive Hypotheses: Local-Weight Models for Decomposition of Evidential Support,” Cognitive Psychology 38, 16–47. {% %} Brenner, Lyle & Yuval Rottenstreich (1999) “Focus, Repacking, and the Judgment of Grouped Hypotheses,” Journal of Behavioral Decision Making 12, 141–148. {% %} Brenner, Menachem & Yehuda Izhakian (2015) “Asset Pricing and Ambiguity: Empirical Evidence,” working paper. {% Applications of Bayesian statistics in medical/biological world. Nice, personal. %} Breslow, Norman (1990) “Biostatistics and Bayes,” Statistical Science 5, 269–298. {% Seems to write also on: total harm of seeding hurricanes is reduced, but they went to Cuba and Castro objected so US stopped. %} Breuer, George (1980) “Weather Modification, Prospects and Problems.” Cambridge University Press, Cambridge. {% suspicion under ambiguity: points it out in the Ellsberg paradox. %} Brewer, K.R.W. (1963) “Decisions under Uncertainty: Comment,” Quarterly Journal of Economics 77, 159–161. {% Discuss Ellsberg experiment when and when not subject can choose color to bet on, so, controlling for suspicion (suspicion under ambiguity). Consider slanting probability only rational in latter case when there is reason for suspicion. %} Brewer, K.R.W. & William Fellner (1965) “The Slanting of Subjective Probabilities—Agreement on Some Essentials,” Quarterly Journal of Economics 77, 657–663. {% An empirical study showing that figures work better than tables. %} Brewer, Noel T., Melissa B. Gilkey, Sarah E. Lillie, Bradford W. Hesse, & Stacey L. Sheridan (2012) “Tables or Bar Graphs? Presenting Test Results in Electronic Medical Records,” Medical Decision Making 32, 532–544. {% %} Brickman, Philip, Dan Coates, & Ronnie Janoff-Bulman (1978) “Lottery Winners and Accident Victims: Is Happiness Relative?,” Journal of Personality and Social Psychology 37, 917–927. {% %} Bridgman, Percy W. (1922) “Dimensional Analysis.” Yale University Press, New Haven (revised edn. 1931.) {% Wonderful book that I borrowed from Bob Nau Citations from Ellsberg (1954): P. 6: we must demand that the set of operations equivalent to any concept be a unique set, for otherwise there are possibilities of ambiguity in practical applications which we cannot admit ... P. 10: if we have more than one set of operations we have more than one concept, and strictly speaking there should be a separate name to correspond to each different set of operations. P. 23 seems to write: “If we deal with phenomena outside the domain in which we originally defined our concepts, we may find physical hindrances to performing the operations of the original definition, so that the original operations have to be replaced by others. These new operations are, of course, to be chosen so that they give, within experimental error, the same numerical results in the domain in which the two sets of operations may be both applied; but we must recognize in principle that in changing the operations we have really changed the concept.” %} Bridgman, Percy W. (1927) “The Logic of Modern Physics.” MacMillan, New York. (8th edn. 1958.) {% Introduces the quadratic proper scoring rule. This is a version of incentive compatibility that preceded Hurwicz (1972). P. 2 nicely points out the very useful fact that n events with relative frequencies p1,…,pn to be given same judged probability on each observation should be given judged probabilities fj = pj to minimize punishment. Also mentions, informally in an example, the difference between calibration and discrimination. What an ideas in three pages! %} Brier, Glenn W. (1950) “Verification of Forecasts Expressed in Terms of Probability,” Monthly Weather Review 78, 1–3. {% statistics for C/E (cost-effectiveness) %} Briggs, Andrew, Mark Sculpher, & Martin J. Buxton (1994) “Uncertainty in the Economic Evaluation of Health Care Technologies: The Role of Sensitivity Analysis,” Health Economics 3, 95–104. {% %} Brinks, Mirjam & Wakker, Peter P. (2012) “Risico is geen Nederlands Woord,” ILink to papernterview in Het Parool 09 Aug. 2012 (National Dutch newspaper). Link to paper
British Association for the Advancement of Science (1933) “Interim Report of the Committee Appointed to Consider and Report upon the Possibility of Quantitative Estimates of Sensory Events,” Report of the Annual Meeting, 277–334. {% Was cited as a good didactical example to illustrate decision analysis. %} Brittain, Jack & Sim Sitkin (1989) “Facts, Figures, and Organizational Decisions: Carter Racing and Quantitative Analysis in the Organizational Behavior Classroom,” Organizational Behavior Teaching Review 14, 62–81, {% %} Broadstock, Marita & Susan Michie (2000) “Processes of Patient Decision Making: Theoretical and Methodological Issues,” Psychological Health 15, 191–204. {% A game between selves at different times, with equilibria resulting. %} Brocas, Isabelle (2011) “Dynamic Inconsistency and Choice,” Theory and Decision 71, 343–364. {% dynamic consistency; information aversion; value of information This paper shows that dynamic inconsistency leads to aversion to information. With some benevolence on the reader’s part, the same result can be inferred from Wakker (1988) “Nonexpected Utility as Aversion of Information,” JBDM 1. Forgone branch independence (mostly called consequentialism nowadays, after Machina 1989) is stated there on p. 173, as part of the “first objection” in §4, reduction of compound lotteries is assumed as self-evident, after which the independence considered by Wakker amounts to dynamic consistency. %} Brocas, Isabelle & Juan D. Carrillo (2000) “The Value of Information when Preferences Are Dynamically Inconsistent,” European Economic Review 44, 1104–1115. {% %} Brock, William A. (1970) “An Axiomatic Basis for the Ramsey Weizsäcker Overtaking Criterion,” Econometrica 38, 927–929. {% utility families parametric; consider all functions which have the sign of derivatives alternating (u' > 0, u'' < 0, etc. Relate them to Laplace transforms of distributions. %} Brockett, Patrick L. & Linda L. Golden (1987) “A Class of Utility Functions Containing all the Common Utility Functions,” Management Science 33, 955–964. {% information aversion?, test for AIDS/Huntington’s disease (I don’t know which) %} Brody, Jane E. (1988) “Personal Health,” The New York Times, August 25, 1988, B17. {% %} Broll, Udo, Kit Pong Wong, & Itzhak Zilcha (1999) “Multiple Currencies and Hedging,” Economica 66, 421–432. {% Use PT to analyze resource allocation, finding that loss aversion is the main factor. %} Bromiley, Philip (2009) “A Prospect Theory Model of Resource Allocation,” Decision Analysis 6, 124–138. {% Test PT with more than three outcomes. Separate gain prospects, loss prospects, and mixed prospects. Their main interest is testing aversion to mean-preserving spreads. The controversial Levy &Levy (2002) did this too. Find usual things of PT, but less loss aversion and less pronounced probability weighting. Fox mixed prospects, the probability of losing does much. losses from prior endowment mechanism: they do that (p. 161). reflection at individual level for risk: p. 171 ff. consider it. They find the usual reflection at the aggregate level. At the individual level they find no clear classifications at all, which seems like H0, but makes them conclude that it may not be at the individual level. %} Brooks, Peter, Simon Peters, & Horst Zank (2014) “Risk Behavior for Gain, Loss, and Mixed Prospects,” Theory and Decision 77, 153–182. {% Use random incentive system. Consider choices between (p:x, r:z, p:x) and (p:x+, r:z, p:x) for all variables nonnegative; i.e., they test aversion to particular mean-preserving spreads, with always x z x. Because these spreads concern mixed prospects, they interpret aversion as loss aversion. They find that most subjects are loss averse, women considerably more than men (gender differences in risk attitudes). They also consider what happens under variations of z without affecting rank-ordering, amounting to tests of comonotonic independence, and find violations there, with more risk aversion as z gets lower. %} Brooks, Peter & Horst Zank (2005) “Loss Averse Behavior,” Journal of Risk and Uncertainty 31, 301–325. {% Argue that biases and WTP-WTA discrepancy can be solved by exercise, feedback and incentives. %} Brookshire, David S. & Don L. Coursey (1987) “Measuring the Value of a Public Good: An Empirical Comparison of Elicitation Procedures,” American Economic Review 77, 554–566. {% equity-versus-efficiency: seems to describe case known as U.S. versus Holmes. Seaman Holmes was involved in throwing people overboard from an overcrowded lifeboat, in 1841. Judge Baldwin found him guilty because he had not done it by lot: “In no other than this or some like way are those having equal rights put upon equal footing” %} Broome, John R. (1984) “Selecting People Randomly,” Ethics 95, 38–55. {% %} Broome, John R. (1985) “The Economic Value of Life,” Economica 52, 281–294. {% R.C. Jeffrey model: reformulates Harsanyi’s theorem for Bolker/Jeffrey restricting representations to subsets: p. 493: points out that a Hammond paper, to apply Gorman’s theorem, requires full product structure, and cites personal communication with Gorman claiming that it could be considerably generalized. %} Broome, John R. (1990) “Bolker-Jeffrey Expected Utility Theory and Axiomatic Utilitarianism,” Review of Economic Studies 57, 477–502. {% This book has been one of the most influential works for my academic thinking. risky utility u = strength of preference v (or other riskless cardinal utility, often called value) discounting normative: §6.2, p. 134, seems to assume, implicitly without further motivation, that discounting is irrational. Book argues that aggregation over uncertainty and maybe also persons and time, should be additivite with respect to one same cardinal index, being “goodness.” Goodness is a kind of cardinal utility (may deviate from preference if latter are irrational). The required separability can be justified by assuming that “all relevant” be incorporated in the outcomes (“individuation of outcomes”). The book gives an advanced discussion of this point in §§5.3-5.7. It points out that separability is not the major weakness of axiomatizations, but rather completeness (which becomes harder the more one individuates), in two ways: (1) incommensurability (I disagree) and (2) incomplete domain (“rectangular field assumption”) with which I agree; some choices are too implausible to be ever hypothetical). risky utility u = strength of preference v (or other riskless cardinal utility, often called value): that the cardinal index should be the same for uncertainty as interpersonally, can be obtained formally from the mathematics of weak separability in both dimensions for matrices (“crosscutting separability,” e.g. if row = person and column = state of nature), by Gorman (1968), leading to additive representability. (The same maths has been used by economists such as Van Daal & Merkies). P. 146/147 (§6.5) will write, on distinctions between various cardinal indexes: “And it is natural to think this an empty distinction.” Download 7.23 Mb. 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