Bibliography
Download 7.23 Mb.
|
| §3 gives a long list of examples of context-dependence, always arguing for the one side of the coin that that can happen and never for the other side of the coin that then not much theory can be developed or predictions be made.
I also disagree with the use of the social choice theory analysis of the author. He first argues that for a social choice relation there is less reason for consistency than individual. Well, OK. Then he revisits Arrow’s impossibility theorem without imposing internal consistency conditions (such as transitivity) on social preference. He does impose Pareto optimality and some other conditions invoking individual preferences. He then says that the conditions invoking individual preferences are external consistency conditions for social preference. Under this heading he derives a few formal axiomatic variations on Arrow’s result. I think that taking the individual prefs as external and not as part of the internal system is ad hoc and the “external consistency” of Pareto optimality, for instance, is not more convincing than the internal consistency condition of transitivity of group preference in a fundamental way. Gives nice example of violation of IIA: from {b,c} you take c, from {a,b,c} you take b. Reason: these are slices of cake and you were taught not to take the largest slice but only the second-largest. %} Sen, Amartya K. (1993) “The Internal Consistency of Choice,” Econometrica 61, 495–521. {% P. 765: Buridan’s ass; paper gives further examples where basic principles of revealed pref. such as IIA are violated, and distinguishes many reasons for those violations. Term menu-independence is used as a nice alternative for Tversky’s context-dependence. Elementary results on revealed pref are given; they don’t seem to be new. Variation of the Luce & Raiffa restaurant example: {t,O} where t is take tea invitation of friend, O is going home. You’re inclined to take t. Then comes {t,O,H} where friend also offers H (heroin) ... Footnote 30 is quite favorable to EU. %} Sen, Amartya K. (1997) “Maximization and the Act of Choice,” Econometrica 65, 745–779. {% foundations of probability: well, its history. How Lewis Carroll and others struggled with the maths of Bayes law and the choice of noninformative priors in many calculation problems. %} Seneta, Eugene (2012) “Victorian Probability and Lewis Carroll,” Journal of the Royal Statistical Society: Series A 174, 435–451. {% discounting normative: seems to argue against discounting. %} Senior, Nassau W. (1836) “An Outline of the Science of Political Economy.” Clowes and Sons, London, UK. {% %} Sennetti, John T. (1976) “On Bernoulli, Sharpe, Financial Risk and the St. Petersburg Paradox,” Journal of Finance 31, 960–962. {% foundations of statistics %} Sennetti, John T. (1995) “On the Incoherent Use of Evidence: Why Subjective Bayesian Evidence Is not Held Probative,” Auditing 14, 193. {% Axiomatizes basically the same model as Klibanoff, Marinacci, & Mukerji (2005) (KMM), but assumes an extra stage with objective extraneous probabilities prior to the model. He thus also considers probability distributions over acts. In this respect he is as the original three-stage of Anscombe & Aumann (1963); they also assumed such a third prior stage. He assumes EU within the extra stage, as he does within all stages of his model (same as KMM), but he abandons reduction of compound lotteries so as to have deviations from EU and to have ambiguity and Ellsberg behavior (with multistage modeling). That is, he abandons the reversal-of-order axiom of Anscombe-Aumann. That reversal-of-order axiom justifies assuming the third prior stage away and moving it into the afterwards-stage. (Most papers using the Anscombe-Aumann model since the 1980s take it, following Fishburn, in the latter sense, and have only objective-probabilities afterwards and not prior.) Seo can use the extra prior probabilities to calibrate, à la matching probabilities, the subjective probabilities over the states. In this way we can recover info about , although need not be unique. Seo thus does not need the unobservable second-order acts of KMM, but in return is less general. He has the same parameters and modeling of ambiguity as KMM. As regards the calibration procedure: if receiving some roulette lottery (that is how I refer to probability distributions over deterministic prizes resulting after the horse-race/states) under event E is equivalent to receiving it over the whole state space with prior probability 1/3, then the second-order integrated subjective probability () over E must also be 1/3. Halevy & Ozenoren have a similar model with probabilistic sophistication instead of EU within each stage, where they put the calibration idea central. %} Seo, Kyoungwon (2009) “Ambiguity and Second-Order Belief,” Econometrica 77, 1575–1605. {% %} Sertel, Murat R. (1972) “A Four-Flagged Lemma,” Review of Economic Studies 39, 487–490. {% Similar to the repetitions approach in Wakker (1986, Theory and Decision). %} Sertel, Murat R. & Arkadii Slinko (2007) “Ranking Committees, Income Streams of Multisets,” Economic Theory 30, 265–287. {% conditional probability; foundations of statistics; ancillary statistics defined regarding “no information about theta” %} Severini, Thomas A. (1995) “Information and Conditional Inference,” Journal of the American Statistical Association 90, 1341–1346. {% Introduces his idea of nonadditive probability (“potential surprise”). The derived decision model does not seem to be interesting (you should group, for a given act, all outcomes with same degree of surprise, and then consider of them only the highest????????). Ch. II insists on differentiating between gains and losses; says that sign-dependence: people first assess gains-part, then losses-part, then aggregate. Seems to argue that statistical information is not relevant to single-shot decisions: (Principle of Complete Ignorance): p. 8 seems to ask as a meant-to-be rhetoric question: “Suppose the captains in a Test Match have agreed that instead of tossing a coin for a choice of innings they will decide the matter by this next throw of a die, and that if it shows an ace Australia shall bat first, if any other number, then England shall bat first. Can we now give any meaningful answer whatever to the question, “Who will bat first?” except “We do not know?” ” Shackle is making elementary mistakes! Arrow (1951 Econometrica p. 419) criticizes Shackle’s theory for it being impossible to incorporate any sense of updating after repeated trials. It seems that Shackle was a student of Keynes. %} Shackle, George L.S. (1949) “Expectation in Economics.” Cambridge University Press, Cambridge. {% Nonadditivity is taken to express amount of information, somewhat like belief functions. Says beliefs must sum to one but potential surprise need not. Draws sharp distinction between indivisible experiment (unique event) and divisible (repeatable). P. 71 seems to argue that probabilities are irrelevant for single events P. 72 claims as self-evident (“The reader will at once, I think, concede”) that, among a number of hypotheses with equal degree of surprise, only the one with the highest gain is of concern to the decision maker. That makes sense to me only if the hypotheses are choice options. Apart from this strange claim of max-only-concern, repeated several times, it always seems that hypotheses are uncertain events. Shackle seems to favor a max-max approach to uncertainty, but discusses also an “integral” solution that he does not like. P. 72/73 argues that you cannot integrate over mutually exclusive hypotheses, which seems totally absurd to me. He describes an integral idea that was described by a Professor Svennilson, but only in Swedish, and was reported to Shackle by a Mr. Turvey. I thought for some time that maybe it referred to a rank-dependent form, but in Copenhagen in 1997, with the help of Jacob Gyntelberg who has Danish as his mother language and therefore can understand some Swedish, read in Svennilson’s work and came to conclude that he probably does not have it. Shackle also seems to assume that gains should be treated separately from losses. %} Shackle, George L.S. (1949) “A Non-Additive Measure of Uncertainty,” Review of Economic Studies 17, 70–74. {% Review of Shackle’s work is presented in Ford (1993). %} Shackle, George L.S. (1968) “Expectations, Investment and Income;” 2nd edn. Oxford University Press, Oxford. {% %} Shackley, Phil & Cam Donaldson (2002) “Should We Use Willingness to Pay to Elicit Community Preferences for Health Care? New Evidence from Using a ‘Marginal’ Approach,” Journal of Health Economics 21, 971–991. {% inverse-S: find it, with overestimation of low probabilities and underestimation of high, but for probability estimates and not for decisions. %} Shaefer, Ralf E. & Katrin Borcherding (1973) “The Assessment of Subjective Probability Distributions: A Training Experiment,” Acta Psychologica 37, 117–129. {% %} Shafer, Glenn (1976) “A Mathematical Theory of Evidence.” Princeton University Press, Princeton NJ. {% %} Shafer, Glenn (1978) “Non-Additive Probabilities in the Work of Bernoulli and Lambert,” Archive of History of Exact Sciences 19, 309–370. {% %} Shafer, Glenn (1979) “Allocations of Probability,” Annals of Probability 7, 827–839. {% conditional probability %} Shafer, Glenn (1982) “Bayes’s Two Arguments for the Rule of Conditioning,” Annals of Statistics 10, 1075–1089. {% conditional probability %} Shafer, Glenn (1985) “Conditional Probability,” International Statistical Review 53, 261–277. {% %} Shafer, Glenn (1986) “Savage Revisited” (including comments) Satistical Science 1, 463–501. {% %} Shafer, Glenn (1987) “Probability Judgement in Artificial Intelligence and Expert Systems,” Statistical Science 2, 3–16. {% %} Shafer, Glenn (1988) “The St. Petersburg Paradox.” In Samuel Kotz & Norman J. Johnson (eds.) Encyclopedia of Statistical Sciences, Vol 8, 865–870, Wiley, New York. {% %} Shafer, Glenn (1990) “Perspectives on the Theory and Practice of Belief Functions,” International Journal of Approximate Reasoning 4, 323–362. {% foundations of probability %} Shafer, Glenn (1993) “Can the Various Meanings of Probability be Reconciled?” In Gideon B. Keren & Charles Lewis (1993, eds.) A Handbook for Data Analysis in the Behavioral Sciences: Methodological Issues, 165–196, Lawrence Erlbaum Publishers, Hillsdale, NJ. {% Nice historical references %} Shafer, Glenn (1996) “The Art of Causal Conjecture.” MIT Press. {% foundations of probability; history of family of Bernoulli; discussing foundations of probability, but at times pleaing for own views and papers. %} Shafer, Glenn (1996) “The Significance of Jacob Bernoulli’s Ars Conjectandi for the Philosophy of Probability Today,” Journal of Econometrics 75, 15–32. {% Formulates a betting criterion that leads to Dempster-Shafer belief functions. %} Shafer, Glenn (2011) “A Betting Interpretation for Probabilities and Dempster-Shafer Degrees of Belief,” International Journal of Approximate Reasoning 52, 127–136. {% %} Shafer, Glenn & Roger Logan (1987) “Implementing Dempster’s Rule for Hierarchical Evidence,” Artificial Intelligence 32, 271–298. {% %} Shafer, Glenn & Amos Tversky (1985) “Languages and Designs for Probability Judgment,” Cognitive Science 9, 309–339. {% revealed preference %} Shafer, Wayne J. (1977) “Revealed Preference Cycles and the Slutsky Matrix,” Journal of Economic Theory 16, 293–309. {% ratio-difference principle: seem to have it. real incentives/hypothetical choice: they use hypothetical choice not real, defend it on p. 350. %} Shafir, Eldar, Peter A. Diamond, & Amos Tversky (1997) “Money Illusion,” Quarterly Journal of Economics 112, 341–374. {% A short summary of models with (deviations from) rationality. %} Shafir, Eldar & Robyn A. LeBoeuf (2002) “Rationality,” Annual Review of Psychology 53, 491–517. {% conservation of influence; This analysis of prisoner’s dilemma is nice illustration, there is apparently perceived to be influence on opponent’s choice prior to his strategy choice (“magical thinking”) but not after. P. 463 on quasi-magical thinking: although people know they can’t influence things, they still act as if: ao about Newcomb’s problem; show that people may cooperate in the prisoner dilemma if uncertain about the strategy choice of the opponent, but defect both if they know that their opponent defects and if they know that their opponent cooperates. In modified experiment, 35% chose both boxes, 65% only one. Funnily, subjects who committed at least two conjunction fallacies (so were more irrational), chose only one box way more often than others. Also about Samuelson’s game, a fifty-fifty lottery for $200 or $100 is done twice. Both if the first gives a win, and if it gives a loss, do people want to take the second. But if they don’t yet know what the first will give they don’t want the second. Similar things for prisoners dilemma. %} Shafir, Eldar & Amos Tversky (1992) “Thinking through Uncertainty: Nonconsequential Reasoning and Choice,” Cognitive Psychology 24, 449–474. {% Consider repeated decisions with outcomes paid each time (experience). If human beings cannot discriminate well between different rewards, then they exhibit the certainty effect. If they can, they exhibit the reversed certainty effect. Animals that can discriminate exhibit the certainty effect. %} Shafir, Sharoni, Taly Reich, Erez Tsur, Ido Erev & Arnon Lotem (2008) “Perceptual Accuracy and Conflicting Effects of Certainty on Risk-Taking Behaviour,” Nature 453, 917–920. {% revealed preference: show violations of revealed preference conditions for animals. %} Shafir, Sharoni, Tom A. Waite, & Brian H. Smith (2002) “Context-Dependent Violations of Rational Choice in Honeybees [Apis Mellifera] and Gray Jays (Perisoreus Canadensis),” Behavioral Ecology and Sociobiology 51, 180–187. {% Subjects can sample from a distribution as in the experienced approach by Erev et al., but in addition get the probability distribution given. Despite the latter, they still sample quite some. %} Shafran, Aric P. (2011) “Self-Protection against Repeated Low Probability Risks,” Journal of Risk and Uncertainty 42, 263–285. {% Tested probability matching for four participants, using real incentives. No probability matching was found; i.e., three out of four participants did the rational thing of always choosing the most likely alternative. %} Shah, Kshitija, Christopher M. Bradshaw, & Elemer Szabadi (1989) “Performance of Humans in Concurrent Variable-Ratio Variable-Ratio Schedules of Monetary Reinforcement,” Psychological Reports 65, 515–520. {% %} Shalev, Jonathan (1997) “Loss Aversion in a Multi-Period Model,” Mathematical Social Sciences 33, 203–236. {% equilibrium under nonEU; brings in prospect theory-like loss aversion %} Shalev, Jonathan (2000) “Loss Aversion Equilibrium,” International Journal of Game Theory 29, 269–287. {% Brings in prospect theory-like loss aversion; does assume invariance w.r.t. scale and location; game theory for nonexpected utility; endogenizes reference point. Its modeling of loss aversion is valuable (with an axiomatization by Peters (2012). March 20, 2014: only now, when rereading Tversky & Kahneman (1991 QJE), a paper I read before around 1990, giving comments to Tversky, I realize that this basic modeling was already in TK91. In particular, their constant sensitivity (p. 1049) serves to keep curvature the same except for the moving of the kink when the reference point moves. %} Shalev, Jonathan (2002) “Loss Aversion and Bargaining,” Theory and Decision 52, 201–232. Probably the published version of: Shalev, Jonathan (1998) “Loss Aversion in Repeated Games,” CORE discussion paper 9814. {% cognitive ability related to discounting: Seems to be a review. When the authors discuss chacen, they mean random incentive system. When they mention reasons for RIS they only mention reduction of payments (p. 298), and do not understand apparently that the main reason is to avoid income effects. %} Shamos, Noah A. & Jeremy R. Gray (2008) “Delay Discounting and Intelligence: A Meta-Analysis,” Intelligence 36, 289–305. {% P. 344: in multiattribute setting (jobs with attributes: salary, authority, interest, influence, status), tradeoffs are weighed more heavily when formulated as losses than as gains. %} Shapira, Zur (1981) “Making Trade-offs between Job Attributes,” Organizational Behavior and Human Performance 28, 331–335. {% %} Shapira, Zur & Itzhak Venezia (1992) “Size and Frequency of Prizes as Determinants of the Demand for Lotteries,” Organizational Behavior and Human Decision Processes 52, 307–318. {% Z&Z; Experiments with students etc. where they play role of insurer or insured. Self-selection occurs to some extent, screening only if there are repetitions and learning. %} Shapira, Zur & Itzhak Venezia (1999) “Experimental Tests of Self-Selection and Screening in Insurance Decisions,” Geneva Papers in Risk and Insurance Theory 24, 139–158. {% Also professional managers of investments display the disposition effect, be it weaker than nonprofessional investors. %} Shapira, Zur & Itzhak Venezia (2001) “Patterns of Behavior of Professionally Managed and Independent Investors,” Journal of Banking & Finance 25, 1573–1587. {% dynamic consistency: in an optimization model, with Artzner et al. risk measures involved, time consistency is defined as optimization that does not depend on counterfactual options. %} Shapiro, Alexander (2009) “On a Time Consistency Concept in Risk Averse Multistage Stochastic Programming,” Operations Research Letters 37, 143–147. {% restricting representations to subsets: shows that characterizing SEU on finite structures is extremely difficult. Many people who, erroneously, think that this amounts to simply restricting Savage’s axioms to the finite case can learn from this paper that it is way more complex. I like the opening in Sections 1 & 2, with good criteria specified: the axiomatization should be on finite sets and for incomplete preferences there. This is what one should do to really understand a model. Such an axiomatization is not yet available for subjective expected utility, so we do not really know what this model means. When I reread this paper March 2011 I was disappointed to see that the author involves artificial compound prospects (he calls it lottery tickets I think) and extendability of the preference relatioin to these. Extendability arguments can be used to assume any desired structural richness, and are of limited interest only. Once you have compound prospects and sequences of outcomes, then easier axiomatizations become possible than provided in this paper. %} Shapiro, Leonard (1979) “Necessary and Sufficient Conditions for Expected Utility Maximizations: The Finite Case, with a Partial Order,” Annals of Statistics 7, 1288–1302. {% measure of similarity %} Shapiro, Monte B. (1961a) “A Method of Measuring Changes Specific to the Individual Psychiatric Patient,” British Journal of Medical Psychology 34, 151–155. {% measure of similarity %} Shapiro, Monte.B. (1961b) “The Personal Questionnaire. Abbreviated Manual.” Unpublished manuscript. {% Nice citation on ambiguity. Interviewed managers. Ch. 4 p. 49, one manager said: “Risk, unlike uncertainty, is manageable.” %} Shapiro, Zur (1995) “Risk Taking: A Managerial Perspective.” Russell Sage Foundation, New York. {% %} Shapley, Lloyd S. (1965) “Notes on n-Person Games VII: Cores of Convex Games,” The RAND Corporation R.M. Reprinted as: Shapley, Lloyd S. (1971) “Cores of Convex Games,” International Journal of Game Theory 1, 11–26. {% %} Shapley, Lloyd S. (1967) “On Balanced Sets and Cores,” Naval Research Logistics Quarterly 14, 453–460. {% %} Shapley, Lloyd S. (1971) “Cores of Convex Games,” International Journal of Game Theory 1, 11–26. {% strength-of-preference representation, for convex subset of reals, with crossover property %} Shapley, Lloyd S. (1975) “Cardinal Utility Comparisons from Intensity Comparisons.” Report R-1683-PR, The Rand Corporation, Santa Monica, California. Reprinted as Appendix 3 to Martin Shubik (1982) “Game Theory in the Social Sciences.” The MIT Press, Cambridge, MA. {% Says that in truncated version of St. Petersburg paradox with 47 tosses and cent as unit, one should pay 24.5 cents and that that price is not at all unreasonable. So, in a truncated version of the St. Petersburg paradox risk neutrality is not unreasonable. %} Shapley, Lloyd S. (1977) “The St. Petersburg Paradox: A Con Game?,” Journal of Economic Theory 14, 439–442. {% %} Shapley, Lloyd S. (1977) “Lotteries and Menus: A Comment on Unbounded Utilities,” Journal of Economic Theory 14, 446–453. {% Dutch book %} Shapley, Lloyd S. (1988) “Axiomatization of Interpersonally Comparable Utilities,” MATH 261, LECTURE NOTES, 1/6/88. {% Views on how to teach the concept of probability to students. %} Sharma, Sashi (2015) “Teaching probability: A Socio-Constructivist Perspective,” Teaching Statistics 37, 78–84. {% Consider a necessity and possibility measure. The ambiguity measure is the difference between the possibility and necessity measure. These can be taken as special cases of upper and lower probabilities. So then the degree of ambiguity of an event is the difference between the upper and lower probability. Walley (1991) called this the imprecision spread. It satisfies all five axioms for ambiguity as a primitive of Fishburn (1993). The measure can similarly be defined for any set of priors other than necessity/possibility, but then not all axioms of Fishburn are satisfied. %} Shattuck, Mark & Carl Wagner (2016) “Peter Fishburn’s Analysis of Ambiguity,” Theory and Decision 81, 153–165. {% Prior to a risky activity (such as sky diving), inexperienced people are more subject to immediacy effect. This paper studies more kinds of impact of risky decisions on intertemporal preference. %} Shavit, Tal, Mosi Rosenboim, & Yaniv Shani (2014) “Time Preference before and after a Risky Activity – A Field Experiment,” Journal of Economic Psychology 43, 30–36. {% survey on nonEU: more precisely, it does what title says, not delving very deep into risk and ambiguity theories themselves. %} Shaw, W. Douglass (2016) “Environmental and Natural Resource Economics Decisions under Risk and Uncertainty: A Survey,” International Review of Environmental and Resource Economics 9, 1–130. {% Use Tradeoff method. %} Shaw, W. Douglass, Rodolfo M. Nagya Jr., & Andres Silva (2006) “Health Benefits and Uncertainty: An Experimental Analysis of the Effects of Risk Presentation on Auction bids for a Healthful Product,” Economics Bulletin 4, 1–8. {% Find that risk aversion for losses correlates with risk aversion for gains. No relation with discounting. losses from prior endowment mechanism; do random incentive system but repeatedly with income effect. %} Shead, N. Will & David C. Hodgins (2009) “Probability Discounting of Gains and Losses: Implications for Risk Attitudes and Impulsivity,” Journal of the Experimental Analysis of Behavior 92, 1–16. {% dynamic consistency; survey of traditional economic discussions, Strotz, Peleg & Yaari, etc. %} Shefrin, Hersh M. (1998) “Changing Utility Functions.” In Salvador Barberà, Peter J. Hammond, & Christian Seidl (eds.) Handbook of Utility Theory, Vol. 1, Principles, 569–626, Kluwer Academic Publishers, Dordrecht. {% Ch. 26 gives a clear definition of Lopes’ SP/A theory. P. 429 last line, incorrectly, claims that probability weighting in SP/A theory would not be sign-dependent. Ch. 27 discusses it more. Unfortunately, there are several confusions. P. 453 2nd para, for instance, writes that in SP/A, with linear utility, risk attitude is captured by probability weighting, which is fine. But the preceding line writes that in prospect theory, where there is both probability weighting and utility curvature, it is different and risk attitude is captured by utility (equate risk aversion with concave utility under nonEU). Why under prospect theory probability weighting would suddenly stop to impact risk attitude, as is suggested here, whereas !the same! probability weighting does under SP/A, is hard to understand, and obviously untrue. There are several confusions of this kind. Never a tradeoff between parsimony and fit is tried. %} Shefrin, Hersh M. (2008) “A Behavioral Approach to Asset Pricing Theory; 2nd edn.” Elsevier, Amsterdam. {% Risk averse for gains, risk seeking for losses: coin the term disposition effect for the phenomenon described in the title. It suggests risk seeking for losses and risk aversion for gains. %} Shefrin, Hersh M. & Meir Statman (1985) “The Disposition to Sell Winners too Early and Ride Losers too Long: Theory and Evidence,” Journal of Finance 15, 777–790. {% time preference %} Shelley, Marjorie K. (1993) “Outcome Signs, Question Frames and Discount Rates,” Management Science 39, 806–815. {% cognitive ability related to discounting: Extensive study showing that steeper discounters are more impulsive. Use hypothetical choice. %} Shenhav, Amitai, David G. R., & Joshua D. Greene (2017) “The Relationship between Intertemporal Choice and Following the Path of Least Resistance across Choices, Preferences, and Beliefs,” Judgment and Decision Making 12, 1–18. {% %} Shenoy, Prakash P. (1998) “Game Trees for Decision Analysis,” Theory and Decision 44, 149–171. {% measure of similarity %} Shepard, Roger N. (1962) “The Analysis of Proximities: Multidimensional Scaling with an Unknown Distance Function, I & II,” Psychometrika 27, 125–140, 219–246. {% measure of similarity %} Shepard, Roger N. (1987) “Toward a Universal Law of Generalization for Psychological Science,” Science 237, September 11, 1317–1323. {% %} Shephard, Ronald W. (1970) “Theory of Cost and Production Functions.” Princeton University Press, Princeton NJ. {% Shows that the Herstein & Milnor (1953) axioms still work on the set of rational (and also dyadic) numbers with one modification: the independence axiom (only for mixture 0.5) has to be imposed with weak preference rather than just indifference. Given generalizations to incomplete preference and to non-Archimedean (Hausner 1954). %} Shepherdson, John C. (1980) “Utility Theory Based on Rational Probabilities,” Journal of Mathematical Economics 7, 91–113. {% Information leakage is a nice term to designate much of what goes on in framing. %} Sher, Shlomi & Craig R.M. McKenzie (2006) “Information Leakage from Logically Equivalent Frames,” Cognition 101, 467–494. {% %} Sherrick, Bruce J., Steven T. Sonka, Peter J. Lamb, & Michael A. Mazzocco (2000) “Decision-Maker Expectations and the Value of Climate Prediction Information: Conceptual Considerations and Preliminary Evidence,” Meteorological Applications 7, 377–386. {% Positions prospect theory and behavioral findings in economics. %} Shiller, Robert J. “Human Behavior and the Efficiency of the Financial System.” In John B. Taylor & Michael Woodford (2000, eds.) Handbook of Macroeconomics, Vol. 1c, Ch. 20, 1305–1340, Elsevier, Amsterdam. {% Surveys, asking people from firms in Japan and the US what they expected about the DJ and Nikkei indexes, and did so for several years. Compare expectations to real performance of indexes. Find that people are strongly more optimistic about their own homestock than the foreigners are. So, at least one group is considerably misjudging. P. 163 argues for importance of asking subjective probability estimates on top of seing real markets. %} Shiller, Robert J., Fumiko Kon-Ya, & Yoshiro Tsutsui (1996) “Why Did the Nikkei Crash? Expanding the Scope of Expectations Data Collection,” Review of Economics and Statistics 78, 156–164. {% value of information: psychological investigation into value of information. One value is instrumental; i.e., when you can improve your future actions because of information. Another value is emotional. That is, also if there is no future action to be influenced by info (no control), still people have preferences or dispreferences over info for its own sake. Many different attitudes are described (coping (“secondary control”)…), and many many references are given. %} Shiloh, Shoshana, Ronit Ben-Sinai, & Giora Keinan (1999) “Effects of Controllability, Predictability, and Information-Seeking Style on Interest in Predictive Genetic Testing,” Personality and Social Psychology Bulletin 25, 1187–1195. {% Dutch book %} Shimony, Abner (1955) “Coherence and Axioms of Confirmation,” Journal of Symbolic Logic 20, 1–28. {% %} Shimony, Abner (1967) “Amplifying Personal Probability Theory: Comments on L.J. Savage’s “Difficulties in the Theory of Personal Probability” ,” Philosophy of Science 34, 326–332. {% Discusses equilibria in games from perspective of trembling hand versus counterfactuals. %} Shin, Hyun Song (1991) “A Reconstruction of Jeffrey’s Notion of Ratifiability in Terms of Counterfactual Beliefs,” Theory and Decision 31, 21–47. {% %} Shin, Hyun Song (1991) “Optimal Betting Odds against Insider Traders,” Economic Journal 101, 1179–1185. {% %} Shin, Hyun Song (1992) “Prices of State-Contingent Claims with Insider Traders, and the Favourite-Longshot Bias,” Economic Journal 102, 426–435. {% %} Shin, Hyun Song (1993) “Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims,” Economic Journal 103, 1141–1153. {% %} Shioji, Naoki & Wataru Takahashi (1988) “Fan’s Theorem Concerning Systems of Convex Inequalities and Its Applications,” Journal of Mathematical Analysis and Applications 135, 383–398. {% Seems to give arguments against efficient market hypothesis. %} Shleifer, Andrei (2000) “Claredon Lectures: Inefficient Markets.” Oxford University Press, Oxford. {% Lists many biases. P. 1080: “The broad field of behavioral economics—perhaps the most important conceptual innovation in economics over the last thirty years—might not have existed without Kahneman and Tversky’s fundamental work.” P. 1081: “My feeling is that the most profound influence of Kahneman and Tversky’s work on economics has been in finance, on what has now become the field of behavioral finance” P. 1081: “large and costly errors people make in important choices. Let me illustrate. First, individuals pay large multiples of actuarially fair value to buy insurance against small losses, as well as to reduce their deductibles (Sydnor 2010).” (small risks overinsured) P. 1081: “Second, the standard economic view that persuasion is conveyance of information seems to run into a rather basic problem that advertising is typically emotional, associative, and misleading—yet nonetheless effective (Bertrand et al. 2010; DellaVigna and Gentzkow 2010; Mullainathan, Schwartzstein, and Shleifer 2008).” “The second objection holds that market forces eliminate the influence of psychological factors on prices and allocations. One version of this argument, made forcefully by Friedman (1953) in the context of financial markets, holds that arbitrage brings prices, and therefore resource allocation, to efficient levels. Subsequent research has shown, however, that Friedman’s argument— while elegant—is theoretically (and practically) incorrect. Real-world arbitrage is costly and risky, and hence limited (see, e.g., Grossman and Miller 1988, DeLong et al. 1990, Shleifer & Vishny 1997). Dozens of empirical studies confirm that, even in markets with relatively inexpensive arbitrage, identical, or nearly identical, securities trade at different prices. With costlier arbitrage, pricing is even less efficient.” P. 1086 writes that reference dependence is the most radical assumption of prospect theory. On the reference point of Kahneman & Tversky versus Köszegi & Rabin: “The reference point is thus left as a rather unspecified part of Kahneman and Tversky’s theory, their measure of “context” in which decisions are made. Köszegi and Rabin (2006) suggest that reference points should be rational expectations of future consumption, a proposal that brings in calculated thought.” This is exactly the point where Köszegi and Rabin (2006) deviate from earlier thoughts. %} Shleifer, Andrei (2012) “Psychologists at the Gate: A Review of Daniel Kahneman’s Thinking, Fast and Slow,” book review of: Kahneman, Daniel (2011) “Thinking: Fast and Slow,” Penguin Books, London. Journal of Economic Literature 2012, 50(4) 1080–1091. {% They consider what happens in experiments on decision under uncertainty if subjects from their own initiative add assumptions about the experiment, as with experimenter demand. Of course, at first almost everything can then be accommodated. They give a theoretical model and look into restrictions. %} Shmaya, Eran Leeat Yariv (2016) “Experiments on Decisions under Uncertainty: A Theoretical Framework,” American Economic Review 106, 1775–1801. {% Argue that Chen, Lakshminarayanan, & Santos’s (2006) finding of loss aversion in Capuchin monkeys may have a different cause, having to do with delay in consumption. Do experiments to confirm it. %} Silberberg, Alan, Peter G. Roma, Mary E. Huntsberry, Frederick R. Warren-Boulton, Takayuki Sakagami, Angela M. Ruggiero & Stephen J. Suomi (2009) “On Loss Aversion in Capuchin Monkeys,” Journal of the Experimental Analysis of Behavior 92, 145–155. {% Stevens, McCabe, & Brazier (2006) is criticized. %} Shmueli, Amir (2007) “It Might be Premature to Reject the Assumption of a Power Curve Relationship between VAS and SG Data: Three Comments on Stevens, McCabe and Brazier's “Mapping between VAS and SG Data; Results from the UK HUI Index 2 Valuation Survey”,” Health Economics 16, 755–758. {% For a number of statements, proposes the ratio of the probability of their intersection by the product of their separate probabilities as index of coherence. It is 1 if the statements are statistically independent. The proposal gave rise to many reactions. %} Shogenji, Tomoji (1999) “Is Coherence Truth-Conducive?,” Analysis 59, 338–345. {% Finds overestimation of small probabilities for losses. Decreases with exposure to market. %} Shogren, Jason F. (1990) “The Impact of Self-Protection and Self-Insurance on Individual Response to Risk,” Journal of Risk and Uncertainty 3, 191–204. {% Study, with usual mug-chocolate stimuli, but also health outcomes, the WTP-WTA discrepancy. Experimentally confirm Hahneman’s (1991) conjecture that substitutable goods, like mugs and chocolates that one can buy everywhere, the discrepancy is smaller than with health outcomes that are not substitutable. For mugs and chocolates, the discrepancy disappears in repeated markets. I did not check the implementations, how those generate reference points. %} Shogren, Jason F., Seung Y. Shin, Dermot J. Hayes, & James B. Kliebenstein (1994) “Resolving Differences in Willingness to Pay and Willingness to Accept,” American Economic Review 84, 255–270. {% %} Shortliffe, Edward H. & Bruce G. Buchanan (1975) “A Model of Inexact Reasoning in Medicine,” Mathematical Biosciences 23, 351–379. {% Psychological study of optimism and pessimism, focusing on higher or on lower outcomes. Self-report questionnaires were used to classify the participants as pessimistic or optimistic. The paper studies which attitude leads to better performances for all kinds of tasks. Could possibly be a ref. for optimism and pessimism in rank-dependence. %} Showers, Carolin (1992) “The Motivational and Emotional Consequences of Considering Positive and Negative Possibilities for an Upcoming Event,” Journal of Personality and Social Psychology 63, 474–484. {% %} Showers, Carolin (1992) “Compartmentalization of Positive and Negative Self-Knowledge: Keeping Bad Apples out of the Bunch,” Journal of Personality and Social Psychology 62, 1036–1049. {% %} Shubik, Martin (1975) “Competitive Equilibrium, the Core, Preferences for Risk and Insurance Markets,” Economic Records 51, 73–83. {% %} Shubik, Martin (1982) “Game Theory in the Social Sciences.” The MIT Press, Cambridge, MA. {% Propose proper scoring rules for multiple choice questions in teaching. Seem to have been the first to show that only the logarithmic proper scoring rule has the property that for more than two events its payment contingent on an event depend only on the subjective probability assigned to that event (pp. 136-137). %} Shuford, Emir H., Arthur Albert, & H. Edward Massengill (1966) “Admissible Probability Measurement Procedure,” Psychometrika 31, 125–145. {% Loss aversion makes prices more rigid. %} Sibly, Hugh (2002) “Loss Averse Customers and Price Inflexibility,” Journal of Economic Psychology 23, 521–538. {% discounting normative: seems to write: “the time at which a man exists cannot affect the value of his happiness from a universal point of view; and […] the interests of posterity must concern a utilitarian as much as those of his contemporaries.” %} Sidgwick, Henry (1907) “The Methods of Ethics.” MacMillan, London. {% P. 96 seems to explain that bookmaking was common term in British race betting. %} Sidney, Charles (1976) “The Art of Legging.” Maxline International, London. {% %} Siebenmorgen, Niklas, Elke U. Weber, & Martin Weber (1999) “Risk Perception in the Short Run and in the Long Run,” Arbeitsbericht, SFB 504, Universität Mannheim. {% information aversion David Pearce pointed out the following reference: Consider the decision whether to be tested for an incurable genetic disorder. A director of a genetic counseling program recently told the New York Times that “there are basically two types of people. There are ‘want-to-knowers’ and there are ‘avoiders.’ There are some people who, even in the absence of being able to alter outcomes, find information of this sort beneficial. The more they know, the more their anxiety level goes down. But there are others who cope by avoiding, who would rather stay hopeful and optimistic and not have the unanswered questions answered.” %} Siebert, Charles (1995) “Living with Toxic Knowledge: The DNA We’ve Been Dealt,” New York Times Magazine, Sept 17. {% P. 1340 suggests that reporting undiscounted results is also worthwhile. %} Siegel, Joanna E., Milton C. Weinstein, Louise B. Russell, Marthe R. Gold (1996, for the Panel on Cost-Effectiveness in Health and Medicine) “Recommendations for Reporting Cost-Effectiveness Analyses,” JAMA 276, 1339–1341. {% risky utility u = strength of preference v (or other riskless cardinal utility, often called value) %} Siegel, Sidney (1956) “A Method for Obtaining an Ordered Metric Scale,” Psychometrika 21, 207–216. {% real incentives/hypothetical choice: Seems to write: “Because of our belief in the central importance of employing payoffs which are meaningful to subjects, rewards which in fact they covet, we have little confidence in experiments in which the ‘payoffs’ are points, credits, or tokens. Or perhaps it would be more accurate to say that we have little confidence in the use of the term payoff to label such trivia. The relevance of such experiments to any theoretical notions about reward, payoff, or utility seems to be dubious.” (p. 148) %} Siegel, Sidney (1964) “Choice, Strategy, and Utility.” McGraw-Hill, New York. {% foundations of statistics: criticizes hypothesis testing. %} Siegfried, Tom (2010) “Odds Are, It’s Wrong: Science Fails to Face the Shortcomings of Statistics,” Science News 177, 26. Available at https://www.sciencenews.org/article/odds-are-its-wrong [129]
Sidgwick,Henry (1907), “The Methods of Ethics;” 7th edn. MacMillan, London. {% %} Sikorski, Roman (1969) “Boolean Algebras;” 3rd edn. Springer, Berlin. {% The authors did crowdsourcing analysis. It means that one same dataset is given to different teams that separately (or in communication) analyze it statistically. They did it with 29 teams, investigating the hypothesis that players in football with a dark skin get more red cards. 20 teams find the result significantly, and 9 teams not. The basic idea may be interesting. Problem is that the result found is unsurprising and uninformative. If one study finds something significant, and another study does not, then this is not a contradictory finding because finding H0 does not mean much and may be just coincidence (unless a good power analysis if added). Whereas for simple t-tests and the like (with simple monotone distributions) there is a clearly best test, for many more complex distributions there is no clearly best statistical test, and different tests have different pros and cons. Comes to it that always subjective choices have to be made in the data analyses, such as what to consider missing. Of the version that I saw (undated, around October 2015) the opening sentence suggests that these authors restrict the scientific process and creativity to empirical/experimental studies: “In the scientific process, creativity is mostly associated with the generation of testable hypotheses and the development of suitable research designs.” %} {% %} Silberzahn, Raphael., Eric Luis Uhlmann, Dan Martin, Pasquale Anselmi, Frederik Aust, Eli C. Awtrey, Štepán Bahník, Feng Bai, Colin Bannard, Evelina Bonnier, Rickard Carlsson, Felix Cheung, Garret Christensen, Russ Clay, Maureen A. Craig, Anna Dalla Rosa, Lammertjan Dam, Mathew H. Evans, Ismael Flores Cervantes, Nathan Fong, Monica Gamez-Djokic. Andreas Glenz, Shauna Gordon-McKeon, Tim J. Heaton, Karin Hederos Eriksson, Moritz Heene, Alicia Hofelich Mohr, Kent Hui, Magnus Johannesson, Jonathan Kalodimos. Erikson Kaszubowski, Deanna Kennedy, Ryan Lei, Thomas Andrew Lindsay, Silvia Liverani, Christopher Madan, Daniel Molden, Eric Molleman, Richard D. Morey, Laetitia Mulder, Bernard A. Nijstad, Bryson Pope, Nolan Pope, Jason M. Prenoveau, Floor Rink, Egidio Robusto, Hadiya Roderique, Anna Sandberg, Elmar Schlueter, Felix S Martin Sherman, S. Amy Sommer, Kristin Lee Sotak, Seth Spain, Christoph Spörlein, Tom Stafford, Luca Stefanutti, Susanne Täuber, Johannes Ullrich, Michelangelo Vianello, Eric-Jan Wagenmakers, Maciej Witkowiak, SangSuk Yoon, & Brian A. Nosek (2015) “Many Analysts, One Dataset: Making Transparent how Variations in Analytical Choices Affect Results,” working paper. {% Textbook on topology. Has an elementary chapter on connected spaces (copy in my archive). Seems to be well written. %} Simmons, George F. (1963) “Introduction to Topology and Modern Analysis.” McGraw-Hill, inc., New York. {% DOI: http://dx.doi.org/10.1177/0956797611417632 Download 7.23 Mb. Share with your friends:
|