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Methoden Technieken

DC = stationarity: p. 2082 comes close: “… various experiments reject exponential discounting on the basis of dynamic choice data finding violations of dynamic consistency.” [Italics from original] He then argues that it may instead be due to an optimistic bias in the expectation of future marginal utility, but I am not sure I understand. %}

Noor, Jawwad (2009) “Hyperbolic Discounting and the Standard Model: Eliciting Discount Functions,” Journal of Economic Theory 144, 2077–2083.

{% In his JET 2009 paper he took discounting model with time-dependent utility. Here he takes outcome-dependent discounting. This is, of course, very unidentifiable, where we can always redefine a new outcome-dependent discount function as simply the product of utility and discounting, with then utility constant 1. He then observes that timed outcomes (only at one time point a nonzero outcome) do not identify discounting. Even if discounting is outcome independent, so not dependent, then such a multiplicative representation indeed gives the utility and discount functions up to a joint power only, leaving power unidentifiable. Standard measurement theorems show that with more than one nonzero outcome, the power and the whole model become identifiable. The author shows how we can derive functional equations from preference, basically by translating into present value. He uses a variation of the Thomsen axiom to get discounting outcome-independent. %}

Noor, Jawwad (2010) “Time Preference Data and Functional Equations,”

{% %}

Noor, Jawwad (2011) “Temptation and Revealed Preference,” Econometrica 79, 601–644.

{% Axiomatizes a model V(x,t) = (x)^tU(x), so constant discounting but with outcome-dependent discount factor. Using my tradeoff technique (writing ~* iso ~^t), the main axiom, weak stationarity, requires that [0,] ~^* [t,T+] requires that these are ~* with respect to the /1 mixture, being [t, T+]. Indeed, (s)⁰/(l)^ = (s)^t/(l)^T+^ requires that these ratios equal (s)^^t/(l)^^T+^. In all of this, the outcomes s and l are used as gauges, so the axiom is necessary. %}

Noor, Jawwad (2011) “Intertemporal Choice and the Magnitude Effect,” Games and Economic Behavior 72, 255–270.

{% %}

Norberg, Tommy (1986) “Random Capacities and Their Distributions,” Probab. Th. Rel. Fields 73, 281–297.

{% %}

Norberg, Tommy & Wilhelmus Vervaat (1989) “Capacities on Non-Hausforff Spaces.” Working paper no. 1989-11 ISSN 0347-2809, Dept. of Mathematics, Chalmers University of Technology, The University of Göteborg, Sweden.

{% utility elicitation; p. 560: domain and framing effects for direct scaling; p. 565 discusses Reflective equilibrium %}

Nord, Erik (1992) “Methods for Quality Adjustment of Life Years,” Social Sciences and Medicin 34, 559–569.

{% utility elicitation %}

Nord, Erik (1994) “The QALY—A Measure of Social Value rather than Individual Utility?,” Health Economics 3, 89–93.

{% Seems to argue that life duration cannot be traded for quality of life. %}

Nord, Erik (2001) “The Desirability of a Condition versus the Well-Being and Worth of a Person,” Health Economics 10, 579–581.

{% foundations of statistics: Plead for using likelihood ratio as strength of evidence, without committing to Bayesianism. So they are pleaing for the likelihood principle; but seem not to cite this principle. Discussions follow in the same issue. %}

Nordgaard, Anders & Birgitta Rasmusson (2012) “The Likelihood Ratio as Value of Evidence—More than a Question of Numbers,” Law, Probability and Risk 11, 303–315.

{% intuitive versus analytical decisions; They show that deliberation for complex choices reduces consistency. For simple choices it does nothing. %}

Nordgren, Loran F. & Ap Dijksterhuis (2008) “The Devil is in the Deliberation: Thinking too Much Reduces Preference Consistency,” Journal of Consumer Research 36, 39–46.

{% criticism of monotonicity in Anscombe-Aumann (1963) for ambiguity: The paper that I saw June 2017 does not go for such criticisms. But it extensively studies prior mixing in the Anscombe-Aumann model. Statewise dominance is like AA monotonicity. %}

Norio, Takeoka (2017) “State-Wise Dominance and Subjective Probabilities,” working paper.

{% Use SG to measure SF-6D. Mention the floor effect of SG that other methods do not have. Find health states that affect utility most. 5% of health states is valued below 0 (death). Argue that this is for Australian health states. Why it would not be for other countries I do not understand. Do not compare to other (such as not SG) methods, but mention that as topic for future research. %}

Norman, Richard, Rosalie Viney, John Brazier, Leonie Burgess, Paula Cronin, Madeleine King, Julie Ratcliffe, & Deborah Street (2014) “Valuing SF-6D Health States Using a Discrete Choice Experiment,” Medical Decision Making 34, 773–786.

{% %}

Norris, Nilan (1976) “General Means and Statistical Theory,”American Statistician 30, 8–13.

{% preference for flexibility %}

Norwood, Franklin B. (2006) “Less Choice Is Better, Sometimes,” Journal of Agricultural & Food Industrial Organization 4, 1–21.

{% Vickrey does better than BDM (Becker-DeGroot-Marschak). %}

Noussair, Charles, Stephane Robin, & Bernard Ruffieux (2004) “Revealing Consumers’ Willingness-to-Pay: A Comparison of the BDM Mechanism and the Vickrey Auction,” Journal of Economic Psychology 25, 725–741.

{% real incentives/hypothetical choice: p. 335 reports no differences.
Measure risk attitude, prudence, temperance, in LISS representative sample of Dutch population (assuming EU), finding these phenomena confirmed. Prudence is positively related with saving, and temperance is negatively related with risky portfolio choices.
decreasing ARA/increasing RRA: p. 355. Their hypothetical choices suggest increasing relative risk aversion.
Have nice discussions of the pros and cons of adding control variables. %}

Noussair, Charles, Stefan T. Trautmann, & Gijs van de Kuilen (2014) “Higher Order Risk Attitudes, Demographics, and Financial Decisions,” Review of Economic Studies 81, 325–355.

{% real incentives/hypothetical choice: find no difference (p. 169).
Use LISS data panel. Risk aversion was measured by five choices between a sure option and a lottery. Religious people are more risk averse. Driven by their different social life more than by religion. %}

Noussair, Charles N., Stefan T. Trautmann , Gijs van de Kuilen, & Nathanael Vellekoop (2013) “Risk Aversion and Religion,” Journal of Risk and Uncertainty 47, 165–183.

{% Finds that people are more risk averse for present payment than for future payment. Focuses on literature from experimental economics, and does not cite works by Prelec & Loewenstein, Keren & Roelofsma, Read, or others. %}

Noussair, Charles & Ping Wu (2006) “Risk Tolerance in the Present and the Future: An Experimental Study,” Managerial and Decision Economics 27, 401–412.

{% Argue that reference point depends on intentions. If you decided before to buy something, you don't perceive the payment of money as a loss. %}

Novemsky, Nathan & Daniel Kahneman (2005) “The Boundaries of Loss Aversion,” Journal of Marketing Research 42, 119–128.

{% %}

Novemsky, Nathan & Daniel Kahneman (2005) “How Do Intentions Affect Loss Aversion?,” Journal of Marketing Research 42, 139–140.

{% This paper presents the Pasadena game:
As in St. Petersburg game, a fair coin is tossed until the first heads shows up. If it is on the nth toss, you receive (1)ⁿ^¹2ⁿ/n, in utility units. So the payments are 2, 2, 2⅔, and so on. A first attempt to calculate EU may concern the limit
lim_n_2^ⁿ  (1)ⁿ^¹2ⁿ/n = 1/2  1/3 + 1/4  1/5 ^... = ln2.
But it is debatable, because both the positive and the negative part have expectation . Hence according to the most common Lebesgue integration, EU is undefined. It can be turned into anything by re-ordering terms. This paper, and several follow-ups by the authors, discuss it. A later paper is Hájek & Nover (2012 Synthese.) %}

Nover, Harris & Alan Hájek (2004) “Vexing Expectations,” Mind 113, 237–249.

{% %}

Novick, Melvin R. & Dennis V. Lindley (1978) “The Use of More Realistic Utility Functions in Educational Applications,” Journal of Educational Measurement 15, 181–191.

{% Discusses vNM utility measurement in a prescriptive vein, recommending interactively. Fixed-state means probability equivalent. %}

Novick, Melvin R. & Dennis V. Lindley (1979) “Fixed-State Assessment of Utility Functions,” Journal of the American Statistical Association 74, 306–311.

{% On bipolar scales. %}

Nowlis, Vincent & Helen H. Nowlis (1956) “The Description and Analysis of Mood,” Annals of the New York Academy of Science 65, 345–355.

{% Seems to have been the first who published Newcomb’s problem, says that the physicist William Newcomb first formulated it. %}

Nozick, Robert (1969) “Newcomb’s Problem and Two Principles of Choice.” In Nicholas Rescher (ed.) Essays in Honor of Carl S. Hempel, 114–146, Reidel, Dordrecht.

{% probability elicitation %}

Nuclear Regulatory Commission, 1975. Reactor Safety Study—An Assessment of Accident Risks in U.S. Commercial Nuclear Power Plants, Report WASH-1400 (NUREG-75/014) NTIS, October.

{% Methoden & Technieken %}

Nunnally, Jum C. (1967) “Psychometric Theory.” McGraw-Hill, New York (2nd edn. 1978).

{% Methoden & Technieken %}

Nunnally, Jum C. & Ira H. Bernstein (1994) “Psychometric Theory;” 9th edn. McGraw-Hill, New York

{% Mathematical results on optmizing EU with power (CRRA) utility. %}

Nutz, Marcel (2012) “Risk Aversion Asymptotics for Power Utility Maximization,” Probability Theory and Related Fields 152, 703–749.

{% Information Technology (IT) project escalation can result from the deaf effect: if the decision maker fails to heed risk warnings communicated by others. This paper investigates how the MRR (messenger (= auditor)-receiver-relation) impacts the deaf effect. If the messenger is collaborative then the deaf effect is smaller than if she is an opponent. They test such things in experiments. I wonder if this could be corrected for trust and selective-reporting-by-the-messenger. For prospect theory, their hypothesis H3a matters. It predicts that the influence of MRR on the deaf effect is weaker for losses than for gains. The idea is that losses give more risk seeking and, hence, more willingness to pursue. I wonder how it is in not considering risk seeking/aversion, but the CHANGE of risk seeking/aversion. Even one level more, the deaf effect itself already is not about the absolute level of risk seeking, but about a CHANGE in risk seeking.
P. 5 1^st para: In escalation situations, people rather add resources to a project after losses so as to recover. Let me add that this is a 2^nd order effect because 1^st order is that things with losses are bad and, hence, are avoided henceforth.
P. 7 1^st column last para: “Student subjects were deemed to be appropriate for this exeriment because framing is a cognitive bias that should not be a function of work experience.” %}

Nuijten, Arno, Mark Keij, & Harry Commandeur (2016) “Collaborative Partner or Opponent: How the Messenger Influences the Deaf Effect in IT Projects,” European Journal of Information Systems 119.

{% foundations of statistics: criticizes hypothesis testing. %}

Nuzzo, Regina (2014) “Scientific Method: Statistical Errors,” Nature 506, 150–152. Available at

http://www.nature.com/news/scientific-methodstatistical-errors-1.14700

. [129]
{% foundations of probability: broad-audience explanation of the central issues. %}

Nuzzo, Regina (2015) “Chance: Peace Talks in the Probability Wars,” NewScientist Physics & Math issue 3012 (March 16 2015) 1–5.
{% probability elicitation: applied to experimental economics; proper scoring rules-correction: elicit subjective probabilities of beliefs about opponents’ strategy choices in a 2 by 2 game. They also estimate such probabilities based on (recency-overweighted) observed choice frequencies of opponent’s choices (fictitious-play beliefs). The subjective probability expressed by a player better predicts his strategy choice than the other probability. Although the authors emphasize this finding much, it is in fact trivial! The subjective probabilities, depicted for instance in Figure 2 on p. 980, are too extreme and variable (and remain so, see top of p. 981), and often are 0 or 1. This suggests that subjects took these as proxies/justifications of what their own strategy choices would be (as per the referee’s/editor’s suggestion in footnote 20 on p. 986), and did not understand the proper scoring rules.
The subjective probability judgments predict the opponent’s strategy choices worse than the observed-frequency estimations (§3.1.3 at pp. 985 ff) according to Brier scores. The linear distance, advanced by the authors in defense of subjective probabilities at the end of §3.1.3, is not proper and should not be considered. For instance, it favors always estimating a probability as 1 as soon as the true probability exceeds 0.5 and, thus, favors extreme judgments rather than true judgments. %}

Nyarko, Yaw & Andrew Schotter (2002) “An Experimental Study of Belief Learning Using Elicited Beliefs,” Econometrica 70, 971–1005.

{% Empirical tests of bargaining solutions %}

Nydegger, Rudy V. & Guillermo Owen (1975) “Two-Person Bargaining: An Experimental Test of the Nash Axioms,” International Journal of Game Theory 3, 239–249.

{% %}

Nygren, Thomas E. (1986) “A Two-stage Algorithm for Assessing Violations of Additivity via Axiomatic and Numerical Conjoint Analysis,” Psychometrika 51, 483–491.

{% %}

Nygren, Thomas E., Alice M. Isen, Pamela J. Taylor, & Jessica Dulin (1996) “The Influence of Positive Affect on the Decision Rule in Risk Situations: Focus on Outcome (and Especially Avoidance of Loss) rather than Probability,” Organizational Behavior and Human Decision Processes 66, 59–72.

{% %}

O’Brien, Bernie J., Michael F. Drummond, Roberta J. Labelle, & Andrew Willan (1994) “In Search of Power and Significance: Issues in the Design and Analysis of Stochastic Cost-Effectiveness Studies in Health Care,” Medical Care 32, 150–163.

{% %}

O’Brien, George L. & Wilhelmus Vervaat (1991) “Capacities, Large Deviations and LogLog Laws.” In Stamatis Cambanis, Gennady Samorodnitsky, & Murad S. Taqqu (eds.) Stable Processes, 43–83.

{% Show that framing matters. %}

O’Connor, Annette M., Norman F. Boyd, David L.Tritchler, Yuri Kriukov, Heather J. Sutherland, & James E. Till (1985) “Eliciting Preferences for Alternative Cancer Drug Treatments: The Influence of Framing, Medium and Rater Variables,” Medical Decision Making 5, 453–463.

{% dynamic consistency; Paper deals with sophisticated choice and naive choice, so it does not consider resolute choice and assumes that dynamic consistency in the strong sense is violated. It assumes constant zero discounting with one exception: the presence, the current period, receives higher weight (present-biased preference). It assumes that one action has to be chosen only one time (e.g. write a report), yielding a cost at some later time and a reward at some, possibly different, later time. §IV considers what the authors call welfare considerations, meaning the undiscounted total utility. This terminology suggests that the authors view zero discounting as normative, an assumption to which I am sympathetic.
For costs, sophistication counters the overweighting of the presence which is always good from the zero-discounting normative perspective (Proposition 3). For current reward, sophistication can do anything, also exacerbate the present-bias (Example 2). E.g. the sophisticated person foresees that he will exhibit presence-bias in the future and therefore consume “too” soon, which decrease in future utility is just enough to make him completely give in to current presence-bias and consume immediately. He thereby lowers the normative undiscounted total utility. %}

O’Donoghue, Ted & Matthew Rabin (1999) “Doing It now or later,” American Economic Review 89, 103–124.

{% %}

O’Donoghue, Ted & Matthew Rabin (1999) “Incentives for Procrastinators,” Quarterly Journal of Economics 114, 769–816.

{% %}

O’Donoghue, Ted & Matthew Rabin (1999) “Risky Behavior among Youths: Some Issues from Behavioral Economics.”

{% %}

O’Donoghue, Ted & Matthew Rabin (1999) “Addiction and Self Control.” In Jon Elster (ed.) Addiction: Entries and Exits, Russel Sage Foundation.

{% %}

O’Donoghue, Ted & Matthew Rabin (1999) “Procrastination in Preparing for Retirement.” In Henry Aaron (ed.) Behavioral Dimensions of Retirement Economics, The Brookings Institution, New York.

{% %}

O’Donoghue, Ted & Matthew Rabin, (2000) “The Economics of Immediate Gratification,” Journal of Behavioral Decision Making 13, 233–250.

{% %}

O’Donoghue, Ted & Matthew Rabin (2001) “Choice and Procrastination,” Quarterly Journal of Economics 116, 121–160.

{% %}

O’Donoghue, Ted & Matthew Rabin (2003) “Studying Optimal Paternalism, Illustrated by a Model of Sin Taxes,” American Economic Review, Papers and Proceedings 93, 186–191.

{% %}

O’Hagan, Anthony, Caitlin E. Buck, Alireza Daneshkhah, J. Riochard Eiser, Paul H. Garthwaite, David J. Jenkinson, Jeremy E. Oakly, & Tim Rakow (2006) “Uncertainty Judgements: Eliciting Experts’ Probabilities.” Wiley, Chichester, England.

{% foundations of probability; foundations of statistics; %}

Oaksford, Mike & Nick Chater (2007) “Bayesian Rationality: The Probabilistic Approach to Human Reasoning.” Oxford University Press, Oxford, UK.

{% Name is also spelled as Occam. Lived between 1285 and 1349, “What can be done with fewer (assumptions) is done in vain with more.” See Paul Edwards (ed., 1967) “The Encyclopedia of Philosophy” 8, MacMillan, New York. %}

Ockham, William of (1285–1347/49)

{% Seems to find loss aversion and reference dependence, and the disposition effect. %}

Odean, Terrance (1998) “Are Investors Reluctant to Realize their Losses?,” Journal of Finance, 1775–1798.

{% With hypothetical choices they find that people discount more with food than with money, both for small and high stakes. %}

Odum, Amy L., Ana A.L. Baumann, & Delores D. Rimington (2006) “Discounting of Delayed Hypothetical Money and Food: Effects of Amount,” Behavioural Processes 73, 278–284.

{% In a careful experiment, ambiguity is generated by balls falling through an irregular Galton box, just created by volunteer students hammering nails in it not knowing for what purpose. This box was used to determine the composition of Ellsberg urns. It is called mechanical ambiguity because it results from a process with no deliberate human beings involved (probably meant: human beings who can rig the urn), and the experimenters not able to know. They compare with ambiguity that is generated by a human being which they call strategic (probably having in mind that this can involve rigging the urn and, hence, they do not control for suspicion and do not allow subjects to choose the color to bet on; suspicion under ambiguity), finding a null hypothesis of no difference (the choice percentages of 37.7% and 45.5% are not significantly different in a between-subject treatment of 53 subjects versus 121 subjects, suffering from the small power of between-subjects designs).
Each subject did only one choice, so as to have no income effects and no need for RIS (which is especially problematic for ambiguity because the risk involved in RIS interferes with ambiguity). The authors also correct for indifference, by letting the ambiguous option being slightly better (to be sure that unambiguous option chosen is really ambiguity aversion) and in another choice situation letting it be slightly worse (to be sure that ambiguous option chosen is really ambiguity seeking). They find some 40% ambiguity aversion but 25% ambiguity seeking (ambiguity seeking). The authors review many studies, showing that their finding is consistent with other findings. They find a null hypothesis of mechanical ambiguity being similar to strategic (human-generated) ambiguity. %}

Oechssler, Jörg & Alex Roomets (2015) “A Test of Mechanical Ambiguity,” Journal of Economic Behavior & Organization 119, 243–246.

{% Test reversal of order axiom of Anscombe & Aumann, and do not reject null of equality. Also find no ambiguity hedging in the AA setting. I take this as evidence against multi-stage acts: those are complex and give noise. The authors assume particular dynamic optimization principles for nonEU in their analyses, similar to Raiffa (1961). %}

Oechssler, Jörg, Hannes Rau, & Alex Roomets (2016) “Hedging and Ambiguity,” working paper.

{% Loss aversion could be an additional factor for the finding of this paper. %}

Offerman, Theo (2002) “Hurting Hurts More than Helping Helps,” European Economic Review 46, 1423–1437.

{% probability elicitation
The authors show how to correct for loss aversion in proper scoring rules. They assume that the reference point is a generalized expected value. Loss aversion is measured empirically. Next the scoring rule is adjusted for loss aversion. An experiment shows good performance. %}

Offerman, Theo & Asa B. Palley (2016) “Lossed in Translation: An Off-the-Shelf Method to Recover Probabilistic Beliefs from Loss-Averse Agents,” Experimental Economics 19, 1–30.

{% %}

Offerman, Theo, Jan Potters, & Joep Sonnemans (2002) “Imitation and Belief Learning in an Oligopoly Experiment,” Review of Economic Studies 69, 973–997.

{% proper scoring rules
The paper reports a control experiment finding H₀ of no difference whether or not subjects are told that the experiment serves to measure beliefs. This was done reluctantly because I only find the approach natural where this is told to the subjects. But a referee required that we add the control experiment and the editor backed him up saying that the paper would be rejected otherwise. Hence we had to add this treatment, which I consider a dilution of the paper. %}

Offerman, Theo, Joep Sonnemans, Gijs van de Kuilen, & Peter P. Wakker (2009) “A Truth Serum for Non-Bayesians: Correcting Proper Scoring Rules for Risk Attitudes,” Review of Economic Studies 76, 1461–1489.

Link to paper
{% decreasing ARA/increasing RRA: find increasing RRA in data set on Pakistani and Indian households. utility concave near ruin: the authors argue that for low-income decreasing RRA is plausible which it, near ruin, indeed is. %}

Ogaki, Masao & Qiang Zhang (2001) “Decreasing Relative Risk Aversion and Tests of Risk Sharing,” Econometrica 69, 515–526.

{% Total utility theory; show that pleasure centers in brain can be directly stimulated. %}

Olds, James & Peter Milner (1954) “Positive Reinforcement Produced by Electrical Stimulation of Septal Area and Other Regions of the Rat Brain,” Journal of Comparative Physiological Psychology 47, 419–427.

{% conservation of influence: takes issue with having decision maker outside of and above the physical world. The big point of the paper is to have the agent as part of the physical world, with all his wishes and decisions generated by the laws of the physical world. Section 3.1.1 defines cellular systems, basically a state of the world making transitions to next states. Then it considers the probability of these transitions maximizing some individual utility functions. The paper writes formulas of Bayes to modify these probabilities, but does not go much beyond that. %}

Oesterheld, Caspar (2016) “Formalizing Preference Utilitarianism in Physical World Models,” Synthese 193, 2247–2759.

{% utility elicitation
p. 270: PE (“N-M) method does worst (SG doesn’t do well); CE (“modified N-M) and Ramsey method (lottery equivalent with .5 probabilities, similar to Davidson, Siegel, & Suppes, 1957) give similar results;
P. 272: Ramsey method was superior in utility analysis;

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