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| §4, nicely, explains how decision theory can be done with marginal utility rather than absolute utility. EU can be calculated doing integration by parts (requiring the distribution function and not just the density function).
The paper in §5 proposes a new parametric family of utility, with marginal utilities specified such that both absolute and relative risk aversion have constant elasticity. There is no closed expression for absolute utility then, the primitive of marginal utility. %} Meyer, Jack (2010) “Representing Risk Preferences in Expected Utility Based Decision Models,” Annals of Operations Research 176, 179–190. {% %} Meyer, Jack & Robert H. Rasche (1992) “Sufficient Conditions for Expected Utility to Imply Mean-Standard Deviation Rankings: Empirical Evidence Concerning the Location and Scale Condition,” Economic Journal 102, 91–106. {% %} Meyer, Richard F (1976) “Preferences over Time.” In Ralph L. Keeney & Howard Raiffa (1976) Decisions with Multiple Objectives, 473–514, Wiley, New York (2nd edn. 1993, Cambridge University Press, Cambridge). {% Suggest use of PT? %} Meyerowitz, Beth E. & Shelly Chaiken (1987) “The Effect of Message Framing on Breast Self-Examination Attitudes, Intentions, and Behavior,” Journal of Personality and Social Psychology 52, 500–510. {% They test stimuli as in Andreoni & Sprenger (2012), but with different correlations. They show that a separation between risk attitude and intertemporal substitution, rather than the certainty effect suggested by A&S, can explain the findings, referring to nonexpected utility theories like Epstein & Zin (1989). %} Miao, Bin & Songfa Zhong (2015) “Risk Preferences Are not Time Preferences: Separating Risk and Time Preference: Comment (#13)” American Economic Review 105, 2272–2286. {% quasi-concave so deliberate randomization: they find this for welfare allocations %} Miao, Bin & Songfa Zhong (2018) “Probabilistic Social Preference: How Machina’s Mom Randomizes Her Choice,” Economic Theory 65, 1–24. {% %} Miao, Jianjun & Neng Wang (2011) “Risk, Uncertainty, and Option Exercise,” Journal of Economic Dynamics and Control 35, 442–461. {% %} Michell, Joel (1986) “Measurement Scales and Statistics: A Clash of Paradigms,” Psychological Bulletin 100, 398–407. {% Seems to have nice discussion of psychological use of additive conjoint measurement. %} Michell, Joel (1990) “An Introduction to the Logic of Psychological Measurement.” Lawrence Erlbaum Associates, Hillsdale, NJ. {% %} Michell, Joel (1993) “The Origins of the Representational Theory of Measurement: Helmholtz, Hölder, and Russell,” Stud. Hist. Phil. Sci. 24, 185–206. {% %} Michell, Joel (1999) “Measurement in Psychology: Critical History of a Methodological Concept.” Cambridge University Press, New York. {% %} Michenaud, Sebastien & Bruno Solnik (2008) “Applying Regret Theory to Investment Choices: Currency Hedging Decisions,” Journal of International Money and Finance 27, 677–694. {% foundations of probability: discusses that in diagnosis uncertainty should be processed through probabilities and Bayes formula. %} Miettinen, Olli S. (2001) “The Modern Scientific Physician: 3. Scientific Diagnosis,” Canadian Medical Association Journal 18, 781–782. {% %} Mijovic-Prelec, Danica & Drazen Prelec (2010) “Self-Deception as Self-Signalling: A Model and Experimental Evidence,” Philosophical Transaction of the Royal Society 365, 227–240. {% Seems to mention -f''//f' as measure for concavity, as Rich Gonzalez told me August 1994 %} Mikusinski, Jan (1948) “Sur les Moyennes de la Forme 1[q(x)],” Studia Mathematica 10, 90–96. {% Seems to be the famous experiment where participants were led to administer high levels of electric shocks to others in fictitious learning experiments. %} Milgram, Stanley (1975) “Obedience to Authority: An Experimental View.” Harper and Row, New York {% common knowledge %} Milgrom, Paul (1981) “An Axiomatic Characterization of Common Knowledge,” Econometrica 49, 219–222. {% Z&Z; gekregen van Harald Uhlig in jan. 1998 %} Milgrom, Paul & John Roberts (1992) “Economics, Organization and Management.” Prentice-Hall, Englewood Cliffs, NJ. {% common knowledge; showed that under common prior assumption willingness to bet against each other cannot be common knowledge. %} Milgrom, Paul & Nancy L. Stokey (1982) “Information, Trade, and Common Knowledge,” Journal of Economic Theory 26, 17–27. {% Work about preference for some numbers. For example, people primarily find 67 aversive, next 53 boring, and then 51 and 49. 87 and 83 are “heavy,” and 22 and 4 are “light.” Erna kreeg ze baan bij afdeling publieksstudies. %} Milikowski, Marisca (1995) “Knowledge of Numbers,” Ph.D. dissertation, Dept. of Psychology, University of Amsterdam. {% Easiest to remember: 8, 1, 100, 2, 17, 5, 9, 10, 99, 11 hardest to remember: 82, 56, 61, 94, 85, 45, 83, 59, 41, 79 good: 10, 100, 36, 8, 24, 66, 16, 4, 1 bad: 37, 93, 41, 51, 39, 17, 13, 59, 29, 43 %} Milikowski, Marisca & Jan J. Elshout (1995) “What Makes a Number Easy to Remember,” British Journal of Psychology 86, 537–547. {% Clients from a dvd rental company will often be more quick to rent a should movie (a useful movie to see) than a want movie (one that is nice to see) but then first watch the want movie and later the should movie. That is, should movies are watched relatively later. The authors interpret this finding as a preference reversal or time inconsistency, such as due to present bias, and as showing that the present bias is bigger for want things than for should things. %} Milkman, Katherine L., Todd Rogers, & Max H. Bazerman (2009) “Highbrow Films Gather Dust: Time-Inconsistent Preferences and Online DVD Rentals,” Management Science 55, 1047–1059. {% foundations of probability: Later editions of Mill (1843) seem to admit the (subjective) more probable than concept and relate it to betting on. (See Daston 1994). %} Mill, John Stuart (1843) “A System of Logic, Ratiocinative and Inductive.” Ed. J. M. Robson, Vols. 7 and 8 of The Collected Works of John Stuart Mill, Toronto/London: University of Toronto Press, 1974. {% ratio bias %} Miller, Dale T., William Turnbull, & Cathy McFarland (1989) “When a Coincidence is Suspicious: The Role of Mental Simulation,” Journal of Personality and Social Psychology 57, 581–589. {% Reformulate Popper’s claims about inductive probability probabilistically. %} Miller, David (1990) “A Restoration of Popperian Inductive Scepticism,” British Journal for the Philosophy of Science 41, 137–140. {% That we can only think in terms of a limited number of categories. %} Miller, George A. (1956) “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information,” Psychological Review 63, 81–97. {% Seems to be an early reference using the choice list to measure indifference. %} Miller, Louis, David E. Meyer, & John T. Lanzetta (1969) “Choice among Equal Expected Value Alternatives: Sequential Effects of Winning Probability Level on Risk Preferences,” Journal of Experimental Psychology 79, 419–423. {% proper scoring rules: the correlation in agents’ private information can be used to induce truthful revelation. P. 1360 left column bottom cites classics on this insight. If we cannot objectively observe if event obtains, we may still have proper scoring rules truth-revealing by letting experts predict other experts’ answers and assuming particular correlations between their beliefs. This is similar to Prelec (2004, Science). %} Miller, Nolan, Paul Resnick, & Richard J. Zeckhauser (2005) “Eliciting Informative Feedback: The Peer-Prediction Method,” Management Science 51, 1359–1373. {% Principal-agent with more productive agent more risk seeking, and ways to seek jobs to identify him. %} Miller, Nolan, Alexander F. Wagner, & Richard J. Zeckhauser (2013) “Solomonic Separation: Risk Decisions as Productivity Indicators,” Journal of Risk and Uncertainty 46, 265–297. {% value of information, à la Kreps & Porteus (1978) and Grant, Kajii, & Polak: extensive survey of psychological investigations into attitudes towards information (e.g. if you can predict in dentist chair what will happen to you or not). Information can also have value if no future actions are influenced by it, to cope with stress for instance. (decision under stress) %} Miller, Suzanne M. (1981) “Predictability and Human Stress: Toward a Clarification of Evidence and Theory.” In Leonard Berkowitz (ed.) Advances in Experimental Social Psychology 14, 203–256, Academic Press, New York. {% probability communication; ratio bias: denominator neglect. They investigate it for CE tasks, where it seems not to have been done before. Relate it to numeracy (Berlin numeracy task); higher numeracy gives more EV maximization, which can be taken as rational. More precisely, it gives less concave utility and more linear probability weighting. Unfortunately, the authors use the T&K92 one-parameter family of probability weighting, so we cannot distinguish between level (optimism) and inverse-S (likelihood insensitivity). P. 2 cites many papers that argue that this is because lower numeracy gives more nonlinear perception. cognitive ability related to likelihood insensitivity (= inverse-S): %} Millroth, Philip & Peter Juslin (2015) “Prospect Evaluation as a Function of Numeracy and Probability Denominator,” Cognition 138, 1–9. {% Relate verbal risk measures to verbal risk behavior. %} Mills, Britain, Valerie F. Reyna, & Steven Estrada (2008) “Explaining Contradictory Relations between Risk Perception and Risk Taking,” Psychological Science 19, 429–433. {% Dutch book %} Milne, Peter (1990) “Scotching the Dutch Book Argument,” Erkenntnis 32, 105–126. {% foundations of probability; foundations of quantum mechanics %} Milne, Peter (1993) “The Foundations of Probability and Quantum Mechanics,” Journal of Philosophical Logic 22, 129–168. {% Presents axioms for the Principle of Complete Ignorance. Characterizes -Hurwicz criterion and similar models. Allows for probabilistic mixing where payments are expectations (p. 55 and footnote 1), which means doing -maxmin with prior mixing and not posterior; prior mixing is more general than posterior. But the mixing is only considered if all nonmixed acts are available, so it is not really -maxmin. %} Milnor, John (1954) “Games against Nature.” In Robert M. Thrall, Clyde H. Coombs, & Robert L. Davis (eds.) Decision Processes, 49–59, Wiley, New York. {% Seem to consider preferences over pairs of acts, much like strengths of preferences, but they interpret it as degree of confidence in preferring one over the other. %} Minardi, Stefania & Andrei Savochkini (2015) “Preferences with Grades of Indecisiveness,” Journal of Economic Theory 155, 300–331. {% updating; Summary of Peter Walley’s ideas, focusing on the mathematical axioms. %} Miranda, Enrique (2008) “A Survey of the Theory of Coherent Lower Previsions,” International Journal of Approximate Reasoning 48, 628–658. {% updating of nonadditive measures %} Miranda, Enrique & Ignacio Montes (2015) “Coherent Updating of Non-Additive Measures,” International Journal of Approximate Reasoning 56, 159–177. {% %} Miron-Shatz, Talya, Yaniv Hanoch, Benjamin A. Katz, Glen M. Doniger, & Elissa M. Ozanne (2015) “Willingness to Test for BRCA1/2 in High Risk Women: Influenced by Risk Perception and Family Experience, rather than by Objective or Subjective Numeracy?,” Judgment and Decision Making 10, 386–399. {% conservation of influence: pp. 13-15 seem to explain that marginal utility was developed in explicit analogy to energetics. %} Mirowski, Philip (1988) “Against Mechanism; Protecting Economics from Science.” Rowman & Littlefield, Totowa, NJ. {% conservation of influence: Bob Nau sent me an email 11Oct90 about this book, which compares utility with potential energy. %} Mirowski, Philip (1989) “More Heat than Light.” Cambridge University Press, New York. {% Seems to point out that correlation of behavior rarely exceeds 0.2 or 0.3. %} Mischel, Walter (1968) “Personality and Assessment.” Wiley, New York. {% Pp. 147-148 seem to point out, in the discussion of a personality coefficient, that the fraction of cross-sectional variation in a specific behavior that can be accounted for by responses to a survey questionnaire typically ranges from .04 to .09. %} Mischel, Walter (1971) “Introduction to Personality.” Holt, Rinehart, and Winston, New York. {% Seems to show that self-control of children waiting for a cookie predicts career-success in later life. %} Mischel, Walter, Yuichi Shoda, & Monica I. Rodriguez (1989) “Delay of Gratification in Children,” Science 244, 933–938. {% They seem to present implicit risk approach: delayed consequences are associated with an implicit risk value %} Mishel, Walter & Joan E. Grusec (1967) “Waiting for Rewards and Punishments: Effects of Time on Probability and Choice,” Journal of Personality and Social Psychology 5, 24–31. {% cognitive ability related to discounting & cognitive ability related to risk/ambiguity aversion: measured immediacy effect and risk aversion (through choices and also BART) (all incentivized) and several introspective indexes of impulsivety. Immediacy effect was related with introspective measures but not with risk aversion. I did not check out how risk aversion was related to introspective measures. %} Mishra, Sandeep & Martin L. Lalumière (2017) “Associations between Delay Discounting and Risk-Related Behaviors, Traits, Attitudes, and Outcomes,” Journal of Behavioral Decision Making 30, 769–781. {% Optimal control problems of central banks. %} Mitchell, Daniel, Haolin Feng, & Kumar Muthuraman (2014) “Impulse Control of Interest Rates,” Operations Research 62, 602–615. {% A meta-meta study on the relation between lab- and field experiments. %} Mitchell, Gregory (2012) “Revisiting Truth or Triviality : The External Validity of Research in the Psychological Laboratory,” Perspectives on Psychological Science 7, 109–117. {% Mention scenario misspecification as a cause of biases. %} Mitchell, Robert C. & Richard T. Carson (1989) “Using Surveys to Value Public Goods: The Contingent Valuation Method.” Resources for the future, Washington DC. {% Their finding may be due to utility curvature, which is more linear for losses than for gains. All choices are hypothetical. %} Mitchell, Suzanne H. & Vanessa B. Wilson (2010) “The Subjective Value of Delayed and Probabilistic Outcomes: Outcome Size Matters for Gains but not for Losses,” Behavioural Processes 83, 36–40. {% Fit hyperbolic and quasi-hyperbolic discount functions to data. %} Mitchell, Suzanne H., Vanessa B. Wilson, & Sarah L. Karalunas (2015) “Comparing Hyperbolic, Delay-Amount Sensitivity and Present-Bias Models of Delay Discounting,” Behavioural Processes 114, 52–62. {% %} Mitra, Tapan & Efe A. Ok (1996) “Personal Income Taxation and the Principle of Equal Sacrifice Revisited,” International Economic Review 37, 925–948. {% %} Mitra, Tapan & Efe A. Ok (1997) “On the Equitability of Progressive Income Taxation,” Journal of Economic Theory 73, 316–334. {% %} Mitra, Tapan & Efe A. Ok (1998) “The Measurement of Income Mobility: A Partial Ordering Approach,” Economic Theory 12, 77–102. {% %} Mitra, Tapan & Efe A. Ok (2007) “Submajorization Type Integral Inequalities Induced by p-Norms,” working paper, submitted to Journal of Mathematical Analysis and Applications. {% %} Mitra, Tapan, Efe A. Ok, & Levent Koçkesen (1998) “Popular Support for Progressive Taxation and the Relative Income Hypothesis,” Economics Letters 58, 69–76. {% %} Miyamoto, John M. (1983) “Measurement Foundations for Multiattribute Psychophysical Theories Based on First Order Polynomials,” Journal of Mathematical Psychology 27, 152–182. {% Lemma 1, p. 443, is very useful because it gives a powerful tool for characterizing linear-exponential and log-power functions. Let U be a continuous strictly increasing function from a subinterval of the positive (positive means 0 is not included) reals to the reals. Let 0.5U(x) + 0.5U(z) = U(y) imply 0.5U(tx) + 0.5U(tz) = U(ty) whenever all arguments are in the domain. Then U is log-power (CRRA). This result is powerful because, first, contrary to virtually all statements in the literature it allows for an arbitary interval as domain and, second, it requires only fifty-fifty mixtures. An immediate corollary, through the transformation x -> ln(x), is: let 0.5U(x) + 0.5U(z) = U(y) imply 0.5U(t+x) + 0.5U(t+z) = U(t+y) whenever all arguments are in the domain. Then U is linear-exponential (CARA). So this also holds on arbitrary intervals. %} Miyamoto, John M. (1983) “An Axiomatization of the Ratio/Difference Representation,” Journal of Mathematical Psychology 27, 439–455. {% %} Miyamoto, John M. (1987) “Constraints on the Representation of Gambles in Prospect Theory,” Journal of Mathematical Psychology 31, 410–418. {% biseparable utility; binary prospects identify U and W %} Miyamoto, John M. (1988) “Generic Utility Theory: Measurement Foundations and Applications in Multiattribute Utility Theory,” Journal of Mathematical Psychology 32, 357–404. {% %} Miyamoto, John M. (1991) “Ordinal Independence and Functional Equations in the Theory of Psychological Difference.” In Jean-Paul Doignon & Jean-Claude Falmagne (eds.) Mathematical Psychology: Current Developments, 3–33, Springer, Berlin. {% P. 203 does not commit to whose preferences should be measured for policy decisions, contrary to the unfortunate suggestions by Gold et al. (1996). paternalism/Humean-view-of-preference: p. 203: assumes EU to be normative, but assumes also that empirical measurement is descriptive and may deviate. %} Miyamoto, John M. (1999) “Quality-Adjusted Life Years (QALY) Utility Models under Expected Utility and Rank Dependent Utility Assumptions,” Journal of Mathematical Psychology 43, 201–237. {% %} Miyamoto, John M., Jason N. Doctor, & Michael J. Perry (2004) “Preference Axioms for a Person Tradeoff Representation.” {% Relates SG to TTO. %} Miyamoto, John M., & Stephen A. Eraker (1985) “Parameter Estimates for a QALY Utility Model,” Medical Decision Making 5, 191–213. {% Test utility independence (of duration from health) and find it mostly confirmed. Only for short durations it’s violated, then subjects do not want to trade off any duration for health. Does utility measurement for nonEU, by restricting stimuli to subdomains where EU is still satisfied, not only for the Miyamoto’s generic utility model which is like rank-dependent utility, but also (p. 16) for prospect theory by avoiding distortions due to sign-dependence. Tradeoff method: p. 198 points out that inconsistencies in revealed preferences which, however, distort utility in a linear manner, are of no concern for utility measurement. This is precisely why scale compatibility does not affect the TO utilities. Distortions in utility measurements that distort utility linearly, are of no concern %} Miyamoto, John M., & Stephen A. Eraker (1988) “A Multiplicative Model of the Utility of Survival Duration and Health Quality,” Journal of Experimental Psychology: General 117, 3–20. {% Investigate utility function for life duration. Find that neither exponential nor power families work well. Do their fitting in John’s generic utility model; i.e., that permits probability transformation. %} Miyamoto, John M. & Stephen A. Eraker (1989) “Parametric Models of the Utility of Survival Duration: Tests of Axioms in a Generic Utility Framework,” Organizational Behavior and Human Decision Processes 44, 166–202. {% state-dependent utility Only after publication the authors discovered that Theorem 1 had been obtained before as Theorem 4 in Ebert (1988, Social Choice and Welfare 5), and Theorem 2 as Ebert’s Theorem 3. %} Miyamoto, John M. & Peter P. Wakker (1996) “Multiattribute Utility Theory without Expected Utility Foundations,” Operations Research 44, 313–326. Link to paper Link to comments (Link does not work for some computers. Then can:
Miyamoto, John M., Peter P. Wakker, Han Bleichrodt, & Hans J.M. Peters (1998) “The Zero-Condition: A Simplifying Assumption in QALY Measurement and Multiattribute Utility,” Management Science 44, 839–849. Link to paper
Modica, Salvatore (1995) “Expected Utility for Decision Making with Subjective Models,” Theory and Decision 39, 157–168. {% Modigliani - Miller view of arbitrage seems to be: drives price to fundamental value as soon as there are some rational investors. %} {% %} Modigliani, Franco & Merton H. Miller (1958) “The Cost of Capital, Corporation Finance and the Theory of Investment,” American Economic Review 68, 261–297. {% foundations of statistics: a provocative paper. Argues that statisticians consider their field to be part of decision theory (many statisticians will feel provoked and will disagree), and that it better become a field about inference. On the basis of this claim, the authors write about their claim: “This presents the foundations of statistics with problems that rival the older controversy in importance and scope.” %} Moey, Richard D. & Jan-Willem Romeijn (2012) “Science and Belief: A Plea for Epistemic Statistics,” working paper. {% Seems that he measured decision time as index of effort that subjects did. For choices between almost indifferent options it was twice as much as between options with a clear preference between them. This provides some counterevidence against the flat-maximum problem signaled by Harrison (1989) and others. %} Moffat, Peter G. (2005) “Stochastic Choice and the Allocation of Cognitive Effort,” Experimental Economics 8, 369–388. {% Complexity refers to the number of outcomes of a prospect. More people are complexity averse than complexity loving. The authors discuss preference for event splitting, which goes in the opposite direction. %} Moffatt, Peter G., Stefania Sitzia, & Daniel John Zizzo (2015) “Heterogeneity in Preferences towards Complexity,” Journal of Risk and Uncertainty 51, 147–170. {% PT, applications %} Mohamed, Rayman (2006) “The Psychology of Residential Developers: Lessons from Behavioral Economics and Additional Explanations for Satisficing,” Journal of Planning Education and Research 26, 28–37. {% anonymity protection This was a special issue of Statistica Neerlandica dedicated to Robert J. Mokken. %} Mokken, Robert J., Peter Kooiman, Jeroen Pannekoek, & Leon C.R.J. Willenborg (1992) “Disclosure Risks for Microdata,” Statistica Neerlandica 46, 49–67. {% anonymity protection %} Mokken, Robert J., Jeroen Pannekoek, & Leon C.R.J. Willenborg (1989) “Micro Data and Disclosure Risks,” CBS Select 5, 181–200; SDU/Publishers, The Hague. {% Seems to have the following citation: I am inclined to offer Mr. Vieweg from Berlin an epic poem, Herrmann and Dorothea … Concerning the royalty we will proceed as follows: I will hand over to Mt. Counsel Böttiger a sealed note which contains my demand, and I wait for what Mr. Vieweg will suggest to offer for my werok. If his offer is lower than my demand, then I take my note back,unopened, and the negotiation is broken. If, however, his offer is hgher, then I willnt ask for more than what is written in the note to be opened by Mr. Böttiger. By Johann Wolfgang von Goethe in a letter on January 16, 1797. %} Moldovanu, Benny & Manfred Tietzel (1998) “Goethe’s Second-Price Auction,” Journal of Political Economy 106, 854–859. {% %} Molenaar, Ivo W. (1980) “An Insurance Policy against Unexpected Data,” Kwantitatieve Methoden 1, 49–74. {% foundations of statistics; discussie in Amsterdam with de Leeuw and Linssen %} Molenaar, Ivo W. (1984) “Bayesiaanse Statistiek en het Meten van Voorkennis,” Kwantitatieve Methoden 13, 5–16. {% %} Molenaar, Ivo W. (1985) “Statistics in the Social and Behavioral Sciences,” Statistica Neerlandica 39, 169–179. {% %} Molenaar, Ivo W. (1988) “Displaying Statistical Information: Ergonomic Considerations.” In Gerrit C. van der Veer & Gijsbertus Mulder (eds.) Human-Computer Interaction: Psychonomic Aspects, Springer, Berlin. {% %} Molenaar, Sjaak, Mirjam A.G. Sprangers, Emiel J.th. Rutgers, Ernest J.T. Luiten, Jan Mulder, Patrick M.M. Bossuyt, Jannes J.E. van Everdingen, Paul Oosterveld, & Hanneke C.J.M. de Haes (2001) “Decision Support for Patients with Early-Stage Breast Cancer: Effects of an Interactive Breast Cancer CDROM on Treatment Decision, Satisfaction, and Quality of Life,” Journal of Clinical Oncology 19, 1676–1687. {% P. 2123: “In the absence of survival and major QL [quality of life] differences, the treatment decision can be made according to the patient’s preference.” P. 2129 discusses to what extent patient decisions can/should be influenced by others, strongly favoring minimal influence. Last para of first column makes a strange claim: “The use of a decision aid did not influence the kind of treatment selected. This is a desirable outcome as the aim of the decision aid is to assist patients in the decision-making process, and not to prescribe a course of action.” I guess no influence means no influence on group average, and need not refer to individual level. Anyway, under this token, decision aiding should not influence decisions and only maybe make patients more happy with the decision taken. I think that the primary purpose is to help give better decisions, and the other is only secondary. %} Molenaar, Sjaak, Frans J. Oort, Mirjam A.G. Sprangers, Emiel J.th. Rutgers, Jan Mulder, Hanneke C.J.M. de Haes (2004) “Predictors of Patients’ Choices for Breast-Conserving Therapy or Mastectomy: A Prospective Study,” British Journal of Cancer 90, 2123–2130. {% P. 135 expresses strong preference for belief-function theory over Bayesian approach. %} Mongin, Philippe (1994) “Some Connections between Epistemic Logic and the Theory of Nonadditive Probability.” In Patrick C. Humphreys (ed.) Patrick Suppes: Scientific Philosopher, Vol. 1, 135–171. {% %} Mongin, Philippe (1995) “Consistent Bayesian Aggregation,” Journal of Economic Theory 66, 313–351. {% state-dependent utility %} Mongin, Philippe (1998) “The Paradox of the Bayesian Experts and State-Dependent Utility Theory,” Journal of Mathematical Economics 29, 331–361. {% %} Mongin, Philippe (2008) “Factoring out the Impossibility of Logical Aggregation,” Journal of Economic Theory 141, 100–113. {% P. 372: interpreting utility as measuring: (i) pleasure and pain; (ii) the satisfaction of the individual’s actual preferences; (iii) the individual’s well-being; (iv) the satisfaction of rational and well informed preferences; P. 4: welfarism: individual utilities contain all the information required to derive collective evaluation rules. Teological: do what is “best,” so break promise if it’s better to break deontological: follow rules, so keep promise because that’s a rule. Download 7.23 Mb. Share with your friends:
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