Literatuur over decision-making

Ben Wilbrink

Gerd Gigerenzer, Ralph Hertwig & Thorsten Pachur (Eds.) (2011). Heuristics. The foundations of adaptive behavior. Oxford University Press. [niet als eBook in KB] info, & contents & abstract to every chapter available.

Gerd Gigerenzer and Wolfgang Gaissmaier (2011). Heuristic Decision Making. Annual Review of Psychology, 62, 451-482. abstract

Kris N. Kirby (2011). An empirical assessment of the form of utility functions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 461-476. abstract

James K. Rilling & Alan G. Sanfey (2011). The Neuroscience of Social Decision-Making. Annual Review of Psychology, 62, 23-48. abstract

Robert Axelrod (1997). The complexity of cooperation. Agent-based models of competition and collaboration. Princeton University Press. isbn 0691015678

C. Emdad Haque 1987). Hazards in a fickle environment: Bangladesh. Kluwer Academic Publishers. isbn 0792348699

ao: Hazardous environment and disastrous impact - Human coping responses to natural hazards- Social class formation and vulnerability of the population: a historical account of human occupance and land resource management - Impacts of riverbank erosion disaster - Toward a sustainable floodplain development strategy

Daniel Kahneman & Amos Tversky (Eds.) (2000). Choices, values, and frames. Cambridge University Press. info

Jerad H. Moxley, K. Anders Ericsson, Neil Charness, Ralf T. Krampe (2013). The role of intuition and deliberative thinking in experts' superior tactical decision-making. Cognition, 124, 72-78. abstract

Lichtenstein, Sarah Lichtenstein & Paul Slovic (Eds) (2006). The construction of preference. Cambridge University Press. isbn 0521542200

G. J. Mellenbergh (1979). De beslissing gewogen. In A. D. Groot. Rede als richtsnoer. Mouton: 's-Gravenhage. 183-196. Liber amicorum.

Vaithilingam Jeyakumar and Alexander Rubinov (Eds) (2004). Continuous optimization. Current trends and modern applications. Springer.

Wim J. van der Linden & Gideon J. Mellenbergh (1978). Coefficients for Tests from a Decision Theoretic Point of View. Applied Psychological Measurement 2, 119-134. abstract

Wim J. van der Linden & Gideon J. Mellenbergh (1977). Optimal Cutting Scores Using A Linear Loss Function. Applied Psychological Measurement 2, 593-599. abstract

Ariel Rubinstein (1998). Modeling bounded rationality. MIT Press. isbn 0262681005 pdf (whole book)

'bounded rationality' is een term van Herb Simon, die ook een kritiek geeft in het laatste hoofdstuk van dit boek.

Gerd Gigerenzer (2007). Gut feelings. The intelligence of the unconscious. Penguin. isbn 9780713997514

Ralph L. Keeney and Howard Raiffa (1976). Decisions with multiple objectives. Preferences and value tradeoffs. Cambridge University Press. isbn 0471465100

Hooker, C. A. Hooker, J. J. Leach & E. F. McClennen (Eds) (1978). Foundations and applications of decision theory. Volume I: Theoretical foundations. Volume II: Epistemic and social applications. Reidel. isbn 9027708428 (I) 9027708444 (II)

Elke U. Weber (1994). From Subjective Probabilities to Decision Weights: The Effect of Asymmetric Loss Functions on the Evaluation of Uncertain Outcomes and Events. Psychological Bulletin, 115, No. 2, 228-242. pdf

Hillel J. Einhorn & Robin M. Hogarth (1978). Confidence in judgment: persistence of the illusion of validity. Psychological Review, 85, 395-416. )

H. Swaminathan, Ronald K. Hambleton & James Algina (1975). A Bayesian decision-theoretic procedure for use with criterion-referenced tests. Journal of Educational Measurement, 12, 87-98. preview

Gebruikt in 1980-artikelen.

Lord, F. M. (1985). Estimating the imputed social cost of errors of measurement. Psychometrika, 50, 57-68.

Fredric M. Lord (1980). Applications of item response theory to practical testing problems. Erlbaum. Ch. 11: Mastery testing.

P. van Rijn, A. Béguin & H. Verstralen (2009). Zakken of slagen? De nauwkeurigheid van examenuitslagen in het voortgezet onderwijs. Pedagogische Studiën, 86, 185-195. abstract

Bastiaan J. Vrijhof, Gideon J. Mellenbergh & Wulfert P. van den Brink (1983). Assessing and Studying Utility Functions in Psychometric Decision Theory. Applied Psychological Measurement, 7, 341-357. abstract

Donald A. Rock, John L. Barone and Robert F. Boldt (1972). A two-stage decision approach to the selection problem. British Journal of Mathematical and Statistical Psychology, 25, 274-282. abstract "Theoretical solutions developed on the computer suggest that a considerable amount of testing time may be saved with little or no decrease in the validity of the selection procedure for all values of the selection ratios."

Hans J. Vos (1997). Adapting the amount of instruction to individual student needs. Educational Research and Evaluation, 3, 79-97. abstract

Henk de Vos (1989). A rational-choice explanation of composition effects in educational research. Rationality and Society, 1, 220-239. (genoemd en gebruikt door Bosker & Guldemond, 1994 abstract

frog-pond effect Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica, 32, 122-136. (genoemd door vd Gaag par 2.3). Reprinted in Tummala, V. M. R, & Henshaw, R. C. (Eds.) (1976). Concepts and applications of modern decision models. Division of Research, Graduate School of Business Administration, Michigan State University, East Lansing, Michigan. Pratt, J. W., Raiffa, H., & Schlaifer, R. (1964). The foundations of decision under uncertainty: an elementary exposition. Journal of the American Statistical Association, 59, 353-375. Tummala, V. M. R, & Henshaw, R. C. (Eds.) (1976). Concepts and applications of modern decision models. Division of Research, Graduate School of Business Administration, Michigan State University, East Lansing, Michigan.

Gideon J. Mellenbergh & Wim J. van der Linden (1978). Decisions based on tests: Some results with a linear loss function. Paper presented at the European Meeting on Psychometrics and Mathematical Psychology, University of Uppsala, Uppsala, Sweden, June 15-17, 1978. Kwantitatieve Methoden, 4, 51-61.

Two questions, reading the abstract: 1) is the resit properly modelled in decision-theoretic terms? 2) Is it really the case that personnel selection is an analogue?

Ad 1.1: the intention is to predict on the basis of the raw test scores.

Ad 1.2. No, correction. The to be ‘predicted’ scores turn out to be true scores of a variable ‘suitable’. How is it possible to predict platonic scores?

Ad 1.3 Introduces a linear loss function, following Mellenbergh & Van der Linden (1977). I will first annotate that one!

Mellenbergh, G.J., & Van der Linden, W.J. (1981). The linear utility model for optimal selection. Psychometrika, 46, 283 - 293.

Wim J. van der Linden & Gideon J. Mellenbergh (1977). Optimal cutting scores using a linear loss function. Applied Psychological Measurement, 1, 593-599.pdf

This is an exercise in reliability, as Wim van der Linden will call it later (1980, Applied Psychological Measurement). Does finding ‘optimal’ cutting scores, given one has ‘fixed in advance’ a latent cutting score solve any real problem? The article might present some useful techniques, or demonstrate some techniques to be not useful at all. Let’s see.

References: Hambleton & Novick (1973); Meskauskas (1976); Huynh (1976).

The analysis will be over the total group of testees. This particular choice is not discussed by the authors. An alternative analysis is to consider only the testees scoring x = c, c being the particular cutting score considered for analysis. Would that model choice have made a difference? Sure: an order of magnitude, much and much simpler, better transparency. See my 1980 in Tijdschrift voor Onderwijsresearch.

Specifying loss functions is a somewhat forced approach. Why not specify utility funtions?

One may wonder how it is possible and why it could be useful to specify utility on a variable that is latent. This is a serious objection; especially so where experimental subjects are being asked to specify their utilities. They will do so, of course, obligingly. (see dissertation Van der Gaag on this issue)

Reference to Huynh (1976) & Mellenbergh, Koppelaar & Van der Linden (1976) for threshold loss analysis: minimizing the risk. I will have to annotate these articles, too: searching for the ancestry of the concepts of loss and risc as used by Mellenbergh & Van der Linden. A shorcut: Mellenbergh & Gode 2005, last chapter on decision-theoretic analysis.

G. J. Mellenbergh & M. Gode (2006). Beslissend testgebruik. In W. P. van den Brink & G. J. Mellenbergh: Testleer en testconstructie (399-427). Boom. isbn 9053522395 info boek

I will comment in English, even though the book is in Dutch. The reason is that I expect the problematic aspects in this chapter to be typical of the decision-theoretic literature in the field of educational measurement.

The chapter identifies Cronbach & Gleser’s classification, allocation and selection, as well as Van der Linden’s (1985) mastery. In the latter case the prediction is of the latent trait or true score. Wow! This is 1977. Totally unacceptable, because is does not offer any practical solution? Let’s see. Mellenbergh & Gode here define allocation as classification; classification with Cronbach & Gleser is categorical (with the testee, not with the treatment): man/woman; healthy/cancer. I really am disappointed, already on the first page of the chapter. Will have to talk to Don about this, I suppose. The Van der Linden ‘mastery’ category is phoney and therefore superfluous (I have shown as much in my 1980 articles). The chapter does not treat the mastery decision at all; why then introduce this dubious distinction?

Utility functions get introduced on p. 405. Regrettably, this introduction is faulty. The text states: “A utility function represents what the ‘results’ are of the selection procedure’ [my translation, b.w.]. Expected utility gets mistaken voor utility. These concepts are categorically different! This is the kind of mistake that is rather typical of the literature on decision making in testing situations, regrettably.

The next problem is dat suitability is declared to be absolute: either the employee turns out to be suitable, or not. This kind of rationalizing is not unusual in selection psychology, yet it is very clumsy and above all it is unnecessary. It is also unnecessary if one has to take pass-fail decisions, as will be the case in, f.e., the situation depicted in Figure 12.1.

Here threshold loss gets introduced. The reference is Hambleton & Novick 1973. I will now annotate that one, it is pretty basic to pretty much all that has been published later on utility models for achievement tests.

Wim J. van der Linden (1985). Decision theory in educational research and testing. In T. Husen & T. N. Postlethwaite (Eds.), International encyclopedia of education: Research and studies (pp. 1328-1333). Oxford: Pergamon Press.

Lee J. Cronbach & Goldine C. Gleser (1957/1965 ). Psychological tests and personnel decisions. University of Illinois Press.

Ronald K. Hambleton & Melvin R. Novick (1972). Toward an integration of theory and method for criterion-referenced tests. ACT Research Report 53. Journal of Educational Measurement, 1973, 10, 159-170. pdf

The basic paradigm, believe or not, is sketched verbally in the following citation. It has been followed stubbornly by many researchers not asking some critical but simple questions. The formal apparatus follows the next description (see the report).

The formal model then gets presented in a formalistic way that makes it rather difficult to understand. Let me therefore first report in my own words what the authors propose here, and the extensions of the model that in my opinion are necessary to avoid any fuzziness..

  1. The situation to be modeled is that of pass-fail scoring, failed students will have to resit the test after some extra preparation time.
  2. The goal variable is mastery, the latent trait or true score that is.
  3. The utility function on mastery is a threshold funtion, f.e. it is zero below the point of sufficient mastery, one above it.
  4. The exact ‘point of sufficient mastery’ is assumed to be known! This is unacceptable, but at this point I will go along with this assumption. A complete decision theoretic approach, of course, should not hinge on this kind fuzziness, but instead resolve it. For example, by using a utility function that is derived from or identical with the learning curve for mastery.
  5. Somehow costs are relevant too according to the authors; they do not explicitly model it, however. Fuzziness again. I will leave costs out of the model altogether.
  6. The model may be applied in the case there is only one student, as well as in the case of groups (in the latter case some Bayesian statistics might be used forfine tuning)
  7. The question then is: given the individual’s test score X=x, should she be passed, or failed?
  8. Some mapping of observed score on the mastery variable is needed. I prefer using a binomial model here, so there is a definite likelihood function on the mastery variable (SPA-model)
  9. Expected utility for the pass decision under threshold utility E(u1) then simply is the probability this individual is a master.
  10. Now the question is: what is the expected utility of the fail-decision? The Hambleton-Novick model is thoroughly fuzzy on this point. Let me try to be specific, then
  11. Assume there is only one resit (it is always possible to extend the model to more resits, see, f.e., Van Naerssen’s tentamenmodel)
  12. After the resit, given de raw score on the resit, the expected utility E(u2) is, again, the probability of mastery.
  13. The problem then becomes: what is the prediction of the score on the resit, given de score on the first test? For an immediate resit the prediction function is the betabinomial function. The situation is more complicated than that, however: the student will spend time learning, heightening her mastery score. This will soon get way too complicated to model abstractly, however.
  14. Assume empirical data to be available — as they should be, of course (a validation study) (otherwise: do the experiment to obtain them) — on the resit-scores given the score on the first test.
  15. Assume a betabinomial distribution (n, a, b) fitted to the resit-score distribution given the score of our individual student X = x. The density function on mastery then is a beta function on parameters a and b, the expected utility E(U2) under threshold utility then is the probability of mastery.
  16. The expected utility of the fail decision given X = x now is E(u2), and that value, of course, is always higher than E(u1) barring extreme contingencies. Therefore: always fail all students, unless X = n.
  17. Wow. How further? Is there only fuzziness?
  18. Plot E(u2) - E(u1) for X = 0, 1 .. n. For an impression of this kind of plot, see the figure.
  19. gif/toetsen_HN.png
  20. A good criterion now might be to set the cutting/passing score X=c at the score c where the difference in expected utilities E(u2) - E(u1) is smaller than the corresponding difference for X = c-1. Assume the plot of differences to be decelerating in the range of interest, and deceleration first to increase and then to decrease. The optimum passing score then is the score corresponding to the inflection point: the number correct at the righ end of the steepest strech. Is this a procedure resulting in the optimal cutting score, within the restrictions of the situation as given? No, but it obviates fuzzy talk about costs. Call this solution ‘satisficing’ (Herbert A. Simon): it is evidently the case ‘better’ models can be developed, but this solution in many cases will do perfectly.
    • Simon introduced the distinction maximizers - satisficers in 'Rational choice and the structure of the environment. Psychological Review, 1956, 63, 129-138 (repr in his Models of thought as well as in his Models of man, social and rational. Wiley, 1957.
  21. Observe that in the above exposition there is no need for talk about ‘false negatives’ or ‘false positives’, or ‘incorrect decisions’. Also this kind of terminology does not belong in science: it is value-laden, better get rid of it.
80gif/80bGrens2.gif The figure is from Wilbrink 1980b, Figure 3. It illustrates the situation pretty well. I did not succeed in 1980 to get rid of the fuzzy ‘costs’ of the resit, however ;-)

The test supposedly is a rather short one, the authors never suggest a specific number of items, however. Yet the model has been used in later years for more serious testing in, for example, higher education. Will that make a difference? Supposedly so, but I do not know of any analyses on the subject (they should be available in the literature, I suppose).

Let me first take a look at the following: “Basically then, the examiner's problem is to locate each examinee in the correct category.” This is problematic, it runs counter to the intention to find an acceptable utility function on the goal variable that is relevant to the situation. The goal variable is not correct classification, it is mastery. The problem then is to optimize the level of mastery, using the instrument of extended instruction/learning and a second test., implying a cutoff score on the first test.

Another problem here is the decision to reduce the criterium variable ‘mastery’ to a dichotomy, for no good reason whatsoever. In fact, no reason is given at all, except implicitly that the talk of the town has it that there should be a very special point on the dimension of mastery: so special, in fact, that we speak of masters for those above this magical point, and non-masters for those still below it. I ridicule the thinking of Hambleton and Novick here, because they are smuggling in threshold utility. A mortgage on the house of decision theoretic test psychology. Categories are, f.e., man-woman; cancer yes-no; cat or dog. What Hambleton and Novick are doing is introducing a pseudo category that seems to come in handy in a situation where pass-fail decisions have to be taken.

See here above also the already familiar mistake of calling an expected utility (or loss) simply utility (or loss). Yet these are fundamentally different. Utility is a function over the goal variable, in this case the goal variable is mastery. Expected utility is what obtains for the options in your decision problem, in this case passing or retaining students with a score X=c. In fact it is really simple: whether the decision is to pass or fail this person, her mastery π stays the same and has one definite utility. Meaning also: there is no way to construct a loss here, there are no differences in utility at all, for this person. Therefore the decision model needs to be developed further: failing the student means she has to sit the test again, after some extra time spent in preparation and thus ameliorating her mastery π. The loss in passing this student is then the absolute difference between the utilities of both levels of mastery.

Allow me one more comment on the sentence cited above. The authors have it that (some) decisions are ‘incorrect’. How can that be? Should other decisions have been taken? This is all very clumsy. If decisions have been taken reckoning with the information available, how is it that they can be ‘incorrect’? Herbert Simon was quite explicit on this point: if two alternatives have expected utilities near each other, choose the one with the somewhat higher expected utility. It might turn out that the outcome is disappointing; does that make the decision ‘incorrect’? I don’t think so. There is quite another problem yet with this decision model: the decision maker is not the student. Yet students will adapt their preparation strategies contingent on where the cutting score will be placed (assuming the difficulty of the test will remain the same). See Van Naerssen (1970), or on this website my SPA-model. For the student as decision maker, the model is also one of threshold utility; assuming a pass will have utility 1, a fail utility 0, expected utility for the student is simply the probability to pass. That probability depends on her mastery. For the institution or the teacher the optimalization problem therefore is quite another one than Hamilton and Novick try to let us believe: it is to find that threshold on the test as well as the retest that will result in the highest mastery (for individuals or for the group of testees) in some sense (expected utility that is).

Naerssen, R. F. van (1965). Enkele eenvoudige besliskundige toepassingen bij test en selectie. Nederlands Tijdschrift voor de Psychologie, 20, 365-380. fc

Hunter, J. E. , & Schmidt, F. L. (1980?). Fitting people to jobs: the impact of personnel selection on national productivity. In Fleishman, E. A. (Ed. ), Human performance and productivity. (COWO?). fc selectie

Chen, J.J., & M.R. Novick (1982) On the use of a cumulative distribution as a utility function in educational or employment selection. Journal of Educational Statistics, 7, 19-35. fc uit het abtract: A least-squares procedure, developed by Lindley and Novick for fitting a utility function, is applied to truncated normal and extended beta distribution functions. The truncated normal and beta distributions avoid the symmetry and infinite range restrictions of the normal distribution and can provide fits in some cases in which the normal functional forms cannot provide a reasonable fit.

Novick & Grizzle (1965). A Bayesian analysis of data from clinical trials. JASA. (fc)

Novick (1980). Statistics as psychometrics. Pm, 45: 411. (fc)

Melvin R. Novick and D. V. Lindley (1979). Fixed-state assessment of utility functions. Journal of the American Statistical Association, 74, 306-310. (fc) preview

This approach may be a useful alternatiev to fixed probability methods, but only in an interactive environment in which the resolution of incoherence is encouraged and facilitated.

Melvin R. Novick and D. V. Lindley (1978). The use of more realistic utility functions in educational applications. Journal of Educational Measurement, 15, 181-191. fc preview

Sluit aan bij de manier waarop Pratt, en ook Schlaifer, nutsfuncties opstellen. De summary:

Michael T. Kane & Robert L. Brennan (1980). Agreement coefficients as indices of dependability for domain-referenced tests. APM, 4, 105-126. (loss functions) pdf

Julius Kuhl (1978). Standard setting and risk preference: an elaboration of the theory of achievement motivation and an empirical test. Psychological Review, 85, 239-248. abstract

N. v.d. Gaag (1990). Empirische utiliteiten voor psychometrische beslissingen. Proefschrift UvA 22 november 1990 (promotor: Don Mellenbergh).

mijn notitie d.d. 4-2002: Dan blijkt dat van proefpersonen heel vreemde dingen worden gevraagd, en dat ze keurige antwoorden geven die bij benadering lineaire nutsfucnties (inderdaad: twee, over ware beheersing) opleveren. Dit zijn experimenten die heel bruikbaar zijn om te illustreren hoe volgzaam proefpersonen zijn (niet alle proefpersonen, trouwens, er is wel een enkele opstandige proefpersoon geweest). Bijzonder problematisch, maar dat gaat al terug tot op het onderzoek van Vrijhof (1981) (zie Psychon aanvraag, 1986, van Mellenbergh), is dat studenten, als student, en docenten, als docent, tot dezelfde nutsfuncties komen. Dat suggereert dat de resultaten van deze onderzoeken artefactueel kunnen zijn

= Gruijter Dato N.M. de Ronald K. Hambleton (1984) On Problems Encountered Using Decision Theory to Set Cutoff Scores APPLIED PSYCHOLOGICAL MEASUREMENT Vol. 8, No. 1, Winter 1984, pp. 1-8 In the decision-theoretic approach to determining a cutoff score, the cutoff score chosen is that which maximizes expected utility of pass/fail decisions. This approach is not without its problems. In this paper several of these problems are considered: inaccurate parameter estimates, choice of test model and consequences, choice of subpopulations, optimal cutoff scores on various occasions, and cutoff scores as targets. It is suggested that these problems will need to be overcome and/or understood more thoroughly before the full potential of1the decision-theoretic approach can be realized in practice. Linden Wim J. van der Some Thoughts on the Use of Decision Theory to Set Cutoff Scores: Comment on de Gruijter and Hambleton APPLIED PSYCHOLOGICAL MEASUREMENT Vol. 8, No. 1, Winter 1984, pp. 9-17 In response to an article by de Gruijter and Hambleton (1984), some thoughts on the use of decision theory for setting cutoff scores on mastery tests are presented. This paper argues that decision theory offers much more than suggested by de Gruijter and Hambleton and that an attempt at evaluating its potentials for mastery testing should address the full scale of possibilities. As for the problems de Gruijter and Hambleton have raised, some of them disappear if proper choices from decision theory are made, while others are inherent in mastery testing and will be encountered by any method of setting cutoff scores. Further, this paper points at the development of new technology to assist the mastery tester in the application of decision theory. From this an optimistic attitude towards the potentials of decision theory for mastery testing is concluded. Dato N. M. de Gruijter Ronald K. Hambleton Reply to van der Linden's "Thoughts on the Use of Decision Theory to Set Cutoff Scores"

Ronald K. Hambleton, Hariharan Swaminathan, James Algina& Douglas Bill Coulson (1978). Criterion-referenced testing and measurement: a review of technical issues and developments. Review of Educational research, 48, 1-47. JSTOR read online free

Authors think in terms of classification. Philosophers would call this an category mistake. The better approach: decision-theoretic without artificial classificatory cutting scores.

Vos, H. J. (1990). Simultaneous optimization of decisions using a linear utility function. Journal of Educational Statistics, 15, 309-340. preview:

W. J. van der Linden (1987). The use of test scores for classification decisions with threshold utility. Journal of Educational Statistics, 12, 62-75. open access

Huynh Huynh (1977). Two simple classes of mastery scores based on the beta-binomial model. Psychometrika, 42, 601-608. !--hardcopy bak dm--> preview

See Huynh (1976) on the idea of the referral task.

Huynh Huynh (1980). A non-randomized minimax solution for passing scores in the binomial error model. Pm, 45, 167. abstract

Huynh Huynh (1982). Assessing efficiency of decisions in mastery testing. JESt, 7, 47-63. preview

False positive error, false negative error.

Huynh Huynh (1976). On the reliability of decisions in domain referenced testing. JEM , 13, 265-276. preview

bivariate beta-binmial model. In fact, an exercise in threshold loss with criterion referenced tests.

Daniel Gigone & Reid Hastie (1997). Proper analysis of the accuracy of group judgments. sychological Bulletin, 123, 149-167. abstract

George K. Chacko (1971). Applied statistics in decision-making. American Elsevier. 0444001093

Conditions for Intuitive Expertise. A Failure to Disagree. Daniel Kahneman & Gary Klein (2009). American Psychologist pdf

Ben R. Newell, David A. Lagnado, David R. Shanks (2015 2nd). Straight choices. The psychology of decision making. Psychology Press. 9781848722835 info [UBL wassweg aanwezig] [Hoewel op een breder publiek gericht, is het wel up to date wat ontwikkelingen betreft]

Hal R. Arkes and Kennth R. Hammond (Eds.) (1986). Judgment and decision making. London: Cambridge University Press. isbn 0521339146 [er is in 1999 een 2e editie verschenen]

Kenneth R. Hammond (2000). Judgments under stress. Oxford University Press. isbn 0195131436 info

Judgments under stress are the kind of decisions leading to disasters such as with the Challenger

C. R. Bell (Ed.) (1979). Uncertain outcomes. MTP Press. isbn 0852001037

W. M. Goldstein & R. M. Hogarth (Eds) (1997). Research on judgment and decision making. Currents, connections, and controversies. Cambridge University Press. isbn 0521483344 info

Robin M. Hogarth (2001). Educating intuition. Chicago: The University of Chicago Press. isbn 0226348601

David T. Chuang, James J. Chen & Melvin R. Novick (1981). Theory and practice for the use of cut-scores for personnel decisions. JESt, 6, 129-152. abstract

James J. Chen & Melvin R. Novick (1982). On the use of a cumulative distribution as a utility function in educational or employment selection. JESt, 7, 19-35. abstract &

Coleman, J. S. (1986). Individual interests and collective action. Selected essays. London: Cambridge UP. UBL: 3594 C12

Cooper, W.S., Decision theory as a branch of evolutionary theory: a biological derivation of the Savage axioms. PR 1987, 94, 395-411.

Charles E. Davis, James Hickman and Melvin R. Novick (1973). A primer on decision analysis for individually prescribed instruction. Iowa City, Iowa: The Research and Development Division / The American College Testing Program. ACT Technical Bulletin no. 17. Not available on the ACT website (Research Reports are, Technical Bulletins not).

Jon Elster & Nicolas Herpin (Eds.) (1994). The ethics of medical choice. London: Pinter. isbn 1855672111

Ferguson, T. S. (1967). Mathematical statistics. A decision theoretic approach. London: Academic Press.

Freeman, A. M., III (1993). The measurement of environmental and resource values. Theory and methods. Washington, D.C.: Resources for the Future. isbn 0915707691

Shaun P. Hargreaves Heap and Yanis Varoufakis (1995). Game theory. A critical introduction. London: Routledge. isbn 0415094038

Ben R. Newell, David A. Lagnado, David R. Shanks (2015 2nd). Straight choices. The psychology of decision making. Psychology Press. 9781848722835 info

Raab, M., & Gigerenzer, G. (2005). Intelligence as smart heuristics. In R. J. Sternberg & J. E. Pretz (Eds.), Cognition and intelligence: Identifying the mechanisms of the mind (pp. 188-207). Cambridge: Cambridge University Press. pdf

McElreath, R., Boyd, R., Gigerenzer, G., Glöckner, A., Hammerstein, P., Kurzban, R., et al. (2008). Individual decision making and the evolutionary roots of institutions. In C. Engel & W. Singer (Eds.), Better than conscious? Decision making, the human mind, and implications for institutions (pp. 325-342). Cambridge, Mass.: MIT Press. pdf

Marsh, B., Todd, P. M., & Gigerenzer, G. (2004). Cognitive heuristics: Reasoning the fast and frugal way. In J. P. Leighton & R. J. Sternberg (Eds.), The nature of reasoning (pp. 273-287). Cambridge: Cambridge University Press.pdf

McGuire, C. B. McGuire & R. Radner (Eds) (1972). Decision and organization. A volume in honor of Jacob Marschak. North-Holland. isbn 0720433134 0444101209

Daniel Kahneman , Paul Slovic & Amos Tversky (Eds) (1982). Judgment under uncertainty: heuristics and biases. Cambridge University Press.

Hogarth, R. M. Hogarth (Ed. 1990). Insights in decision making. A tribute to Hillel J. Einhorn. University of Chicago Press. isbn 0226348563 — 356 pp. paperback near mint

met oorspronkelijke bijdragen, dus geen reader

Lichtenstein, Sarah Lichtenstein & Paul Slovic (Eds) (2006). The construction of preference. Cambridge University Press. isbn 0521542200

Original contributions!    

Paul K. Moser (Ed.) (1990). Rationality in action. Contemporary approaches. Cambridge University Press. isbn 0521385989

Daniel Kahneman & Amos Tversky (Eds.) (2000). Choices, values, and frames. Cambridge University Press. isbn 9780521627498 info

Ariel Rubinstein (1998). Modeling bounded rationality. MIT Press. isbn 0262681005 pdf (whole book)

Roger J. Bowden (1989). Statistical games and human affairs; the view from within. Cambridge: Cambridge University Press. isbn 0521361788.

Een interessante analye van het probleem van nonrespons (hfdst 2 en 3), en van het eerder al eens door Hofstee aangekaarte probleem van respondenten die de doelen van de onderzoeker anticiperen. Een razend belangrijk onderwerp is dat van de predictive games, waar de statisticus publieke voorspellingen doet. Dat associeert onmiddellijk met Pygmalion-effecten, self-fulfilling prophecies, en in het algemeen de onverantwoordelijk grote sturingskracht die leraren in het onderwijs hebben mbt loopbanen van leerlingen, juist ook langs deze weg van predictive games. Je zou van mijn ATM kunnen zeggen dat het juist de aardige eigenschap heeft deze voorspellingen te doorbreken, door er de neutrale voorspellingen op basis van proeftoetsen voor in de plaats te zetten.

Kenneth R. Hammond (1996). Human judgment and social policy. Irreducible uncertainty, inevitable error, unavoidable injustice. Oxford University Press. isbn 0195097343

Charles Vlek (1973). Notes on the integration of decisionmaking and problem-solving research. Charles Vlek (1973). Psychological studies in probability and decision makin (210-226). dissertation Leiden.

Richard Nisbett and Lee Ross (1980). Human inference: Strategies and shortcomings of social judgment. Prentice-Hall. [decision making]

Gigerenzer G, Gaissmaier W, Kurz-Milcke E, Schwartz LM, Woloshin S: Helping doctors and patients make sense of health statistics. Psychol Sci Public Interest 2008, 8:53-96. OpenURL


Dimov, C.M., Marewski, J. N., & Schooler, L. J. (2017). Architectural process models of decision making: Towards a model database. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (pp. 1931-1936). Austin, TX: Cognitive Science Society. download

Kenneth J. Arrow (1984). Individual choice under certainty and uncertainty. Collected papers of Kenneth J. Arrow, volume 3. The Belknap Press of Harvard University Press. 0674137620 (ao.: Utilities, attitudes, choices: A review note, 55-84. Utility and expectation in economic behavior, 117-146. The theory of risk aversion, 147-171. Exposition of the theory of choice under uncertainty, 172-208. Risk perception in psychology and economics, 261-270.)

Judgment and Decision Making Baruch Fischhoff and Stephen B. Broomell Vol. 71, 2020, pp. 331–355 free

Schmidt, F. L. , Hunter, J. E. , McKenzie, R. C. , & Muldrow, T. W. (1979). Impact of valid selection procedures on work-force productivity. Journal of Applied Psychology, 64, 609-626.

Hunter, John E., and Frank L. Schmidt (1982). Fitting people to jobs: The impact of personnel selection on national productivity. In Marvin D. Dunnette and Edwin A. Fleishman (Eds): Human performance and productivity: Human capability assessment (p. 233-284). Hillsdale, New Jersey: Lawrence Erlbaum Ass. (scan)

Schmidt, F. L. , Hunter, J. E. , Outerbridge, A. N. , & Trattner, M. H. (1986). The economic impact of job selection methods on size, productivity, and payroll costs of the federal work force: an empirically based demonstration. Personnel Psychology, 39, 1-29.

Novick, M. R. (1980). Statistics as psychometrics. Psychometrika, 45, 411- 424. abstract

Nancy S. Petersen (1976). An expected utility model for 'optimal' selection. Journal of Educational Statistics, 1, 333-358. 10.2307/1164987 preview

Raju, N.S., M.J. Burke, & J. Normand (1990). A new approach for utility analysis. Journal of Applied Psychology, 75, 3-12.

Daniel Kahneman. A perspective on judgment and choice: Mapping bounded rationality. Or his December 8 2002 Nobel prize lecture: Maps of bounded rationality: A perspective on intuitive judgment and choice. pdf. Daniel Kahneman (2003). Experiences of collaborative research. American Psychologist. 58, 723-730. Zie ook zijn 2002 autobiografische artikel (een korte versie van het AP-artikel?):

Daniel Kahnman (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58, 697-720 10.1037/0003-066X.58.9.697 abstract

Leonard Green & Joel Meyerson (2003). A discounting framework for choice with delaed and probabilistic rewards. PB, 130, 769-792. abstract

E. Brandstätter, Gerd Gigerenzer, Ralph Hertwig (2006). The priority heuristic: Making choices without trade-offs. Ps. Rev., 113,409-432

F. J. Anscombe & R. J. Aumann (1963). A definition of subjective probability. AnnMathStat 34: 199. open

Arkes, H.R. (1991). Costs and benefits of judgment errors: implications for debiasing. PB, 110, 486-498.

O.a. over de werkelijkheidswaarde van laboratoriumstudies.

Hal R. Arkes & Laura Hutzel (2000). The role of probability of success estimates in the sunk cost effect. Journal of Behavioral Decision Making, 13, 295-306. abstract

Jonathan Baron (1997). Biases in the quantitative measurement of values for public decisions. Psychological Bulletin, 122, 72-88. utility text

DeFinetti (1970). Logical foundations and measurement of subjective probability. Acta Ps, 129-145. 10.1016/0001-6918(70)90012-0 abstract

Hillel J. Einhorn & Robin M. Hogarth (1981). Behavioral decision theory: processes of judgment and choice. AnnRevPs 32, 53-88. 10.1017/CBO9780511598951.008 abstract

. Deze tekst is best de moeite waard om even door te nemen, omdat de auteurs zich voor de taak gesteld zagen in het kort uit te leggen waar deze belsiskunde over gaat, wat het belang is, welke de filosofische en andere vooronderstellingen.

Berg, N., & Gigerenzer, G. (2007). Psychology implies paternalism? Bounded rationality may reduce the rationale to regulate risk-taking. Social Choice and Welfare, 28, 337-359.

Goldstein, D. G., & Gigerenzer, G. (1999). The recognition heuristic: How ignorance makes us smart. In G. Gigerenzer, P. M. Todd, & the ABC Research Group., Simple heuristics that make us smart (pp. 37-58). New York: Oxford University Press.

Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75-90.

Hertwig, R., & Gigerenzer, G. (1999). The "conjunction fallacy" revisited: How intelligent inferences look like reasoning errors. Journal of Behavioral Decision Making, 12, 275-305.

Chase, V. M., Hertwig, R., & Gigerenzer, G. (1998). Visions of rationality. Trends in Cognitive Sciences, 2, 206-214.

Gigerenzer, G., & Todd, P. M. (1999). Fast and frugal heuristics: The adaptive toolbox. In G. Gigerenzer, P. M. Todd, & the ABC Research Group., Simple heuristics that make us smart (pp. 3-34). New York: Oxford University Press.

Gigerenzer, G., & Selten, R. (2001). Rethinking rationality. In G. Gigerenzer, & R. Selten (Eds.), Bounded rationality: The adaptive toolbox. Dahlem Workshop Report (pp. 1-12). Cambridge, Mass.: MIT Press.

Gigerenzer, G., & Kurzenhäuser, S. (2005). Fast and frugal heuristics in medical decision making. In R. Bibace, J. D. Laird, K. L. Noller, & J. Valsiner (Eds.), Science and medicine in dialogue: Thinking through particulars and universals (pp. 3-15). Westport, CT: Praeger.

Gigerenzer, G., Krauss, S., & Vitouch, O. (2004). The null ritual: What you always wanted to know about significance testing but were afraid to ask. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 391-408). Thousand Oaks: Sage.

Gigerenzer, G., & Goldstein, D. G. (1999). Betting on one good reason: The Take The Best heuristic. In G. Gigerenzer, P. M. Todd, & the ABC Research Group., Simple heuristics that make us smart (pp. 75-95). New York: Oxford University Press.

Gigerenzer, G., & Edwards, A. (2003). Simple tools for understanding risks: From innumeracy to insight. British Medical Journal, 327, 741-744.

Gigerenzer, G., Czerlinski, J., & Martignon, L. (1999). How good are fast and frugal heuristics? In J. Shanteau, B. Mellers, & D. Schum (Eds.), Decision science and technology: Reflections on the contributions of Ward Edwards (pp. 81-103). Boston: Kluwer.

Gigerenzer, G. (2006). Heuristics. In G. Gigerenzer & C. Engel (Eds.), Heuristics and the law (pp. 17-44). Cambridge, Mass.: MIT Press.

Gigerenzer, G. (2006). Bounded and rational. In R. J. Stainton (Ed.), Contemporary debates in cognitive science (Contemporary Debates in Philosophy No. 7) (pp. 115-133). Oxford, UK: Blackwell.

Gigerenzer, G. (2005). I think, therefore I err. Social Research, 72, 195-218.

Gigerenzer, G. (2004). Striking a blow for sanity in theories of rationality. In M. Augier & J. G. March (Eds.), Models of a man: Essays in memory of Herbert A. Simon (pp. 389-409). Cambridge, Mass.: MIT Press.

Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33, 587-606.

Gigerenzer, G. (2004). Fast and frugal heuristics: The tools of bounded rationality. In D. Koehler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making (pp. 62-88). Malden: Blackwell.

Gigerenzer, G. (2003). Where do new ideas come from? A heuristics of discovery in the cognitive sciences. In M. C. Galavotti (Ed.), Observation and experiment in the natural and social sciences (Boston Studies in the Philosophy of Science No. 232) (pp. 99-139). Dordrecht: Kluwer.

Gigerenzer, G. (2002). In the year 2054: Innumeracy defeated. In P. Sedlmeier & T. Betsch (Eds.), Etc.: Frequency processing and cognition (pp. 55-66). Oxford: Oxford University Press.

Gigerenzer, G. (2001). The adaptive toolbox: Toward a Darwinian rationality. In J. A. French, A. C. Kamil, & D. W. Leger (Eds.), Nebraska Symposium on Motivation: Vol. 47. Evolutionary psychology and motivation (Current theory and research in motivation No. 47) (pp. 113-143). Lincoln: University of Nebraska Press.

Scheibehenne, B. (2008). The effect of having too much choice. Doctoral dissertation, Humboldt-Universität zu Berlin, Germany.

Sedlmeier, P., & Gigerenzer, G. (2000). Was Bernoulli wrong? On intuitions about sample size. Journal of Behavioral Decision Making, 13, 133-139. v Sedlmeier, P., & Gigerenzer, G. (2001). Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General, 130, 380-400.

Todd, P. M., & Gigerenzer, G. (2007). Mechanisms of ecological rationality: Heuristics and environments that make us smart. In R. I. M. Dunbar & L. Barrett (Eds.), The Oxford handbook of evolutionary psychology (pp. 197-210). Oxford: Oxford University Press.

Zhu, L., & Gigerenzer, G. (2006). Children can solve Bayesian problems: The role of representation in mental computation. Cognition, 98, 287-308.

Hull, J., P. G. Moore & H. Thomas (1973). Utility and its measurement. JRStSoc A, 136, 226-247. 10.2307/2345110 JSTOR

Tversky, A., and D. Kahneman (1992): “Advances in prospect theory: Cumulative representation of uncertainty,” Journal of Risk and Uncertainty, 5, 297-323. pdf Ook in Kahneman & Tversky (2000)

Kahneman, D. (1992). Reference points, ancjors, norms and mixed feelings. Organizational behavior and decision processes, 51, 296.

Kahneman, D., & Tversky, A. (197?). The psychology of preferences. Scientific American? 136-149.

Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. PR.

Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47: 263. (fc) prospect theory is een alternatief voor expected utility theory. Ook in Kahneman & Tversky 2000. Ook in Moder (1990).

Kahneman, D., & Tversky, A. (1982). On the study of statistical intuitions. Cogn., 11, 123-141. (fc)

Kahneman, D., & Tversky, A. (1982). Variants of uncertainty. Cogn. 11. 143-157. (fc)

Liberman, V., & Tversky, A. (1993). On the evaluation of probability judgments: calibration, resolution, and monotonicity. PB, 114, 162-173.

Lindley, Tversky & Brown (1979). On the reconciliation of probability assessments. JRStSoc A, 142:,146-180.

Shaver, G., & A. Tversky (1985). Languages and designs for probability judgment. CognSc, 9, 309-339

Amos Tversky and Daniel Kahneman (1971). Belief in the law of small numbers. Psychological Bulletin, 76, 105-110. html


Tversky, A., & D. Kahneman (1980). Causal schemas in judgments. In Progress in Soc Ps vol. 1 (E-12)

Tversky, A., & Koehler, D. J. (1994). Support theory: a nonextensional representation of subjective probability. PsRev, 101, 547-567.

Tversky, A., S. Sattath, & P. Slovic, Contingent weighting in judgment and choice. PR 1988, 95, 385-316

Amos Tversky & Daniel Kahneman (1983). Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. PR, 90, 293. 10.1037/0033-295X.90.4.293 abstract

Van belang voor thematiek van combineren van slaagkansen.

Tversky, A., & Fox, C. R. (1995). Weighing risk and uncertainty. Psychological Review, 102, 269-283. fc Ook in Kahneman & Tversky (2000).

ATM: Zou het kunnen dat ik met mijn model de risks evaluaeer zodat de student die zou kunnen gebruiken, terwijl in dezelfde bslissingssituatie zonder het model de student op basis van uncertainty moet opereren? Dat zou een zinvol discussiepunt opleveren, zij het ook theoretisch omdat het ATM in de praktijk immers niet door studdenten zal worden gebruikt. Dan blijft staan dat in de praktijk voor de studenten dus de onzekerheidssituatie zal blijven gelden, met de consequenties die de onderzoekliteratuur daaraan lijkt te verbinden:

TVERSKY, Amos Tversky & Daniel Kahneman (1977). Causal thinking in judgment under uncertainty. In R. E. Butts & J. Hintikka (Eds.), Foundational problems in the special sciences (p. 315-343). Dordrecht: Reidel (download ). 10.1007/978-94-017-0837-1_11 abstract

= Tversky (1972). Elimination by aspects: a theory of choice. PR, 79, 281-29. (fc) abstr: Most probabilistic analyses of choice are based oil theassumption of simple scalability which is ail ordinal formulation of the principle of independence from irrelevant alternatives. This assumption, however, is shown to be illadequate on both theoretical and experimental grounds. To resolve this problem, a more general theory of choice ba5ed oil a covert elimination process isdeveloped. In this theory, each alternative is viewed asa set ofaspects. At each stage ill tile process, an aspect is selected (with probability proportional to its weight), and all the alternatives that do not include the selected aspect are eliminated. The process continues until all alternatives but one are eliminated, It isshown (a) that this model is expremible purely in terms, of the choice alternatives without any reference to specific aspects, (b) that it can be tested using observable choice probabilities, and (c) that it generalizes the choice models of R. D. Luce and of F. Restle. Empirical support from a study of psychophysical and preferential judgments is presented. The strategic implications of the present development are sketched, and the logic of elinliflation by aspects is discussed from both psychological and decision- theoretical viewpoints.

Tversky, A. (1974). Assessing uncertainty. JRoyalStatSoc Series B 148-159. 10.1111/j.2517-6161.1974.tb00996.x JSTOR

Tversky, A., & S. Sattath (1979). Preference trees. PR, 86, 542-573.

Griffin, D., & Tversky, A. (1992). The weighing of evidence and the determinants of confidence. CognPs, 24, 411-435.

Elster, J. (Ed.) (1986). Rational choice. Cambridge: Cambridge University Press. D. Parfit, Prudence, morality, and the prisoner’s dilemma. A. Sen, Behaviour ad the concept of preference [Economica, 1973, 40, 241-59]. J. C. Harsanyi, Advances in understanding rational behavior in R. E. Butts and J. Hintikka (ed), Foundational problems in the special sciences (Dordrecht 1977), pp. 315-343.]. G. Becker, The economic approach to human behavior [G. Becker, The economic approach to human behavior (Chicago University Press, 1976), 3-14.]. A. Tversky & D. Kahneman, The framing of decisions and the psychology of choice [Science 211 (1981), pp. 453-8]. J. G. March, Bounded rationality, ambiguity, and the engineering of choice [Bell Journal of Economics, 9 (1978), 587-608]. R. Boudon, The logic of relative dfrustration [The unintended consequences of social action. Presses Universitaires de France 1981]. S. Popkin, The political economy of peasant society [S. popkin, The rational peasant. University of California Press, 1979, 35-72.]. D. North, A neoclassical theory of the state [D. C. North, Structure and change in economic history. Norton.] Aardig allemaal, maar ik heb geen materiaal gevonden waarover ik aantekeningen had te maken. Er is een paperback-editie van ca ƒ40, gezien bij Kooyker.

Janoff-Bulman, R., & Brickman, Ph. (1982). Expectations and what people learn from failure. In Feather, N. T. (Ed.). Expectations and actions: expectancy-value models in psychology (p. 207-237). Hillsdale, New Jersey: Lawrence Erlbaum. p. 220: Somewhat surprisingly, there is actually no consensus in the psychologival literature on how important it is for individuals to be able to distinguish between controllable and uncontrollable outcomes, and no general theory which makes this ability its central feauture. Wortman and Brehm (1975) suggest that when outcomes are truly uncontrollable, the most adaptive response may be to give up or not to try to exert control in the first place. [Wortman, C. B., and Brehm, J. W. (1975). Responses to uncontrollable outcomes: An integration of reactance theory and the learned helplessness model. In L. Berkowitz (Ed.). Advances in experimental social psycholoy. Vol. 8, New York: Academic Press] But these authors, as well as Wortman (1976), also cite reports that victims of natural disasters or diseases blame themselves or others for these events as evidence of an understandable and healthy tendency for people to believe that these events are controllable rather than random and uncontrollable. [Wortman, C. B. (1976). Causal attributions and personal control. In J. H. Harvey, W. J. Ickes, & R. F. Kidd (Eds.), New directions in attribution research. Vol 1. Hillsdale, New Jersey: Lawrence Erlbaum Associates.] Likewise, Janoff-Bulman (1979) interprets self-blame by rape victims as healthy effort by victims to believe that they can, by their own careful and responsible behavior, prevent the recurrence of the trauma of rape, though rape episodes may in fact be essentially independent of victim behavior. [Janoff-Bulman,R. (1979). Characterological versus behavioral self-blame: Inquiries into depression and rape. Journal of Personality and Social psychology, 37, 1789-1809.] Langer (1975) and Seligman (1975) are unequivocal in taking the position that it is valuable for individuals to believe that they have control when in fact they do not and this belief is an illusion. [Langer, E. J. (1975). The illusion of control. Journal of Personality and Social psychology, 32, 311-328.] [SeligmanM. E. P. (1975). Helplessness. San Francisco: Freeman.] Furby (1979) surveys the psychological literaure on perceived control and finds a general bas toward the belief that that one has control is good. [Furby, L. (1979). Individualistic bias in studies of locus of control. In A. R. Buss (Ed.), Psychology in social context. New York: Halsted.] It is really quite remakrable that the literature has been zo sensitive tot the consequences of people's mistakenly assuming that they do not have control and so insensitive to the consequences of people's mistakenly assuming that they do have control. p. 220: The belief that people are personally resposible for their own successes and failures, that they are in control of their own fates, rconciles people towards accepting their own lot and the lot of others as fair and just (Lerner, 1975). [Lerner,M. J. (1975). The justice motive in social behavior. Journal of Social issues, 31, 1-20.] I this domain, as in others, the culture appears to have two messages, one of them widely publicized and available to all and the other largely hidden and available only to the elect or the elite. The public message is that one should persist and persevere no matter how discouraging things appear, that good things will eventually follow if one only stays on the job, that th only real failure is to stop trying. people who follow orders need not be socialized to decide for themselves when to start and whe to stop, but only to persist at whatever tasks they are assigned. Theirs not to reason why, theirs just to do or die. For the managerial elite, however, discretion rather than blind perseverance is the requisite social virtue. People responsible for making decisions and committing social resources to the realizations of these decisions must, in theory at least, learn how to quit, how to call off disastrous enterprises, how to avoid being trapped in unpromising situations. Children of workers must learn to persist because changing careers, exploring alternatives, and failing are luxuries they cannot afford. Children of the elite not only have the cushion of parental resources that enable them to fail, but have access through these resources to a kind of learning - the ability to discriminate among tasks - that is vital to future success and not available to their less fortunate peers.

Krzysztofowicz, R. (1983). Risk attitude hypotheses of utility theory. In B. P. Stigum & F. Wenstop, Foundations of utility and risk theory with applications. Dordrecht: Reidel. 201-216.

Krzysztofowicz, R. (1983). Strength of preference and risk attitude in utility measurement. Organizational Behavior and Human Performance, 30, 88-113.

Jon Elster (1989). Solomonic judgements. Studies in the limitations of rationality. Cambridge: Cambridge University Press. loten p. 1: My concern here is ... with failures in rational choice theory. p. 2: in this book ... the emphasis is on the indeterminacy of rational vhoice theory. p. 37 I shall argue that we have a strong reluctance to admit uncertainty and indeterminacy in human affairs. Rather than accept the limits of reason, we prefer rituals of reason. p. 114; Using a weighted lottery (or multiple queues) could increase everybody’s chance of getting the scarce good, if the inequality created opportunities or incentives that in the end would make the good less scarce. The regulation of access to medical or technical education by a weighted lottery could be justified by this argument. Ik kan deze gedachte van Elster niet volgen. p. 121; The basic rreason for using lotteries to make decisions is honesty. [Leuk: dat onderscheid tussen fairness en honesty!) Honesty requires us to recognize the pervasiveness of uncertainty and incommensurability, rather than deny or avoid it. Some decisions are going to be arbitrary and epistemically random no matter what we do, no matter how hard we try to base them on reasons. Chance will regulate a large part of our lives, no matter how hard we try to avoid it. By taming chance we can bring the randomness of the universe under our control as far as possible and keep free of self-deception as well. The requirements of personal causation [De Charms (1968)] and autonomy [Elster (1983a), ch. 3] are reconciled by the conscious use of chance to make decisions when rational argument fails. Although the bleakness of this vision may disturb us, it is preferable to a life built on the comforting falsehood that we can always know what to do. Otto Neurath characterizes the belief that we can always have good reasons for our decisions as pseudorationalism. Whereas Cartesian ‘rationalism sees its chief triumph in the clear recognition of the limits of actual insight’, pseudorationalism ‘leads partly to self-deception, partly to hypocrisy’. To conclude the present chapter I can do no better than to quote his further comments on this distinction: [ik neem alleen het tweede deel van het citaat over] “Let us go back to the parable of Descartes. For the wanderers lost in the forest, who have no indication at all as to which direction to follow, it is most important to march on energetically. One of them is driven in some direction by instinct, another by an omen; a third will carefully consider all eventualities, weigh all arguments and counter-arguments and, on the basis of inadequate premises of whose deficiencies he is unaware, take one definitte direction which he considers the correct one. The fourth, finally, will think as well as he can, but not refrain from admitting that his insight is too weak, and quietly allow himself to decide by lot. Let us assume that the chances of getting out of the forest are the same for the four wanderers; nevertheless there will be people whose judgment of the behaviour of the four is very different. To the seeker after truth whose esteem of insight is hghest, the behaviour of the last wandere will be congenial, and that of the pseudorationalist most repellent. In these four kinds of behaviour we can perhaps see four stages of development of mankind without exactly claiming that each one of them has come into full existence. (Neurath, 1913, 9-11).

Ranald R. Macdonald (1986). Credible conceptions and implausible probabilities. BrJMStPsychol, 39, 15-27.

McKenzie, C. R. M. (1994). The accuracy of intuitive judgment strategies: covariation assessment and Bayesian inferene. Cognitive Psychology, 26, 209-239.

Onderzoek in de Kahneman & Tversky lijn, zou je kunnenz zeggen. De taak is schatten van de correlatie in een twee-bij-twee tabel.

MORGENSTERN, OSKAR (1979). SOME REFLECTIONS ON UTILITY. In M. Allais & O. Hagen (eds.). Expected Utility and the Allais Paradox, 175-183. D. Reidel Publishing Company. abstract he whole book: download

M. Allais & O. Hagen (eds.) (1979). Expected Utility Hypotheses and the Allais Paradox: Contemporary Discussions of the Decisions under Uncertainty with Allais’ Rejoinder download

Robert E. Nisbett, David H. Krantz, Christopher Jepson & Ziva Kunda (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90, 339-363. 10.1037/0033-295X.90.4.339 abstract

Roskam, E. E. Ch. I. (1985). Formele benaderingen van keuze- en beslissingsgedrag. NTvdPs 1985, 40, 321-347. [ik heb een kopie]

Leonard J. Savage (1971). Elicitation of personal probabilities and expectations. JASA, 68, 783-800. pdf

Becker, S. W., & S. Siegel (1962). Utility and level of aspiration. American Journal of Psychology, 75, 115-120.

Bepaalt nutsfuncties van studenten voor grades.

Becker, S. W., and Siegel, S. (1958). Utility of grades: level of aspiration in a decision theory context. Journal of Experimental Psychology, 55, 81-85.

Sidney Siegel (1957). Level of aspiration and decision making. Psychologicl Review, 64, 253-262. 10.1037/h0049247 abstract

Slovic, Fischhoff, Lichtenstein (1977). Behavioral decision theory. AnnRevPsychol, 28, 1019. fc

Paul Slovic (1995). The construction of preference. American Psychologist, 50, 364-371. pdf

"People’s preferences and how they report them are remarkably labile. They are exquisitely sensitive to how questions are asked and to the mode of response allowed."

Patrick Suppes (1979). The logic of clinical judgment: Bayesian and other approaches. In H. T. Engelhart, Jr., S. F. Spicker, and B. Towers (eds). Clinical judgment: a critical appraisal. 145-159. Dordrecht: Reidel (download: . [ik heb een fc]. Met repliek van Martin E. Lean p. 161-166. preview

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