decision-theory   102

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Social Decision Theory: Choosing within and between Groups | The Review of Economic Studies | Oxford Academic

We study the behavioural foundation of interdependent preferences, where the outcomes of others affect the welfare of the decision maker. These preferences are taken as given, not derived from more primitive ones. Our aim is to establish an axiomatic foundation providing the link between observation of choices and a functional representation which is convenient, free of inconsistencies and can provide the basis for measurement. The dependence among preferences may take place in two conceptually different ways, expressing two different views of the nature of interdependent preferences. The first is Festinger's view that the evaluation of peers' outcomes is useful to improve individual choices by learning from the comparison. The second is Veblen's view that interdependent preferences keep track of social status derived from a social value attributed to the goods one consumes. Corresponding to these two different views, we have two different formulations. In the first, the decision maker values his outcomes and those of others on the basis of his own utility. In the second, he ranks outcomes according to a social value function. We give different axiomatic foundations to these two different, but complementary, views of the nature of the interdependence. On the basis of this axiomatic foundation, we build a behavioural theory of comparative statics within subjects and across subjects. We characterize preferences according to the relative importance assigned to gains and losses in social domain, that is pride and envy. This parallels the standard analysis of private gains and losses (as well as that of regret and relief). We give an axiomatic foundation of interpersonal comparison of preferences, ordering individuals according to their sensitivity to social ranking. These characterizations provide the behavioural foundation for applied analysis of market and game equilibria with interdependent preferences."
altruism  decision-theory  # 
november 2018 by MarcK
Odds & Ends: Introducing Probability & Decision with a Visual Emphasis
A textbook introducing philosophy students to probability, decision theory, and the philosophical foundations of statistics.
book  statistics  probability  logic  decision-theory 
october 2018 by spl
Ambiguous Correlation | The Review of Economic Studies
"Many decisions are made in environments where outcomes are determined by the realization of multiple random events. A decision maker may be uncertain how these events are related. We identify and experimentally substantiate behavior that intuitively reflects a lack of confidence in their joint distribution. Our findings suggest a dimension of ambiguity which is different from that in the classical distinction between risk and “Knightian uncertainty.”"
ambiguity  yoram.halevy  larry.epstein  decision-theory  **  restud  economics-papers 
february 2018 by MarcK
Stein's example - Wikipedia
Stein's example (or phenomenon or paradox), in decision theory and estimation theory, is the phenomenon that when three or more parameters are estimated simultaneously, there exist combined estimators more accurate on average (that is, having lower expected mean squared error) than any method that handles the parameters separately. It is named after Charles Stein of Stanford University, who discovered the phenomenon in 1955.[1]

An intuitive explanation is that optimizing for the mean-squared error of a combined estimator is not the same as optimizing for the errors of separate estimators of the individual parameters. In practical terms, if the combined error is in fact of interest, then a combined estimator should be used, even if the underlying parameters are independent; this occurs in channel estimation in telecommunications, for instance (different factors affect overall channel performance). On the other hand, if one is instead interested in estimating an individual parameter, then using a combined estimator does not help and is in fact worse.


Many simple, practical estimators achieve better performance than the ordinary estimator. The best-known example is the James–Stein estimator, which works by starting at X and moving towards a particular point (such as the origin) by an amount inversely proportional to the distance of X from that point.
nibble  concept  levers  wiki  reference  acm  stats  probability  decision-theory  estimate  distribution  atoms 
february 2018 by nhaliday
Are Sunk Costs Fallacies? -
But to what extent is the sunk cost fallacy a real fallacy?
Below, I argue the following:
1. sunk costs are probably issues in big organizations
- but maybe not ones that can be helped
2. sunk costs are not issues in animals
3. sunk costs appear to exist in children & adults
- but many apparent instances of the fallacy are better explained as part of a learning strategy
- and there’s little evidence sunk cost-like behavior leads to actual problems in individuals
4. much of what we call sunk cost looks like simple carelessness & thoughtlessness
ratty  gwern  analysis  meta-analysis  faq  biases  rationality  decision-making  decision-theory  economics  behavioral-econ  realness  cost-benefit  learning  wire-guided  marginal  age-generation  aging  industrial-org  organizing  coordination  nature  retention  knowledge  iq  education  tainter  management  government  competition  equilibrium  models  roots  chart 
december 2017 by nhaliday
Buridanic competition - ScienceDirect
"We analyze a model of two-attribute competition for a decision maker who follows a non-compensatory choice procedure that only responds to ordinal rankings along the two dimensions. The decision maker has an outside option that functions as a default alternative. In the absence of a dominant alternative, the decision maker may stick to the default even if it is dominated – capturing the phenomenon of choice procrastination in the presence of difficult choices. We show that the prevalence of difficult-choice situations in equilibrium is related to the magnitude of the choice procrastination effect. In general, features of the choice procedure that are typically viewed as biases tend to “protect” the decision maker, in the sense that they encourage competitors to offer higher-value alternatives in equilibrium. We discuss the potential implications of this analysis for recent discussions of “default architecture”."
rani.spiegler  IO  economics  markets  decision-theory 
december 2017 by MarcK
Kelly criterion - Wikipedia
In probability theory and intertemporal portfolio choice, the Kelly criterion, Kelly strategy, Kelly formula, or Kelly bet, is a formula used to determine the optimal size of a series of bets. In most gambling scenarios, and some investing scenarios under some simplifying assumptions, the Kelly strategy will do better than any essentially different strategy in the long run (that is, over a span of time in which the observed fraction of bets that are successful equals the probability that any given bet will be successful). It was described by J. L. Kelly, Jr, a researcher at Bell Labs, in 1956.[1] The practical use of the formula has been demonstrated.[2][3][4]

The Kelly Criterion is to bet a predetermined fraction of assets and can be counterintuitive. In one study,[5][6] each participant was given $25 and asked to bet on a coin that would land heads 60% of the time. Participants had 30 minutes to play, so could place about 300 bets, and the prizes were capped at $250. Behavior was far from optimal. "Remarkably, 28% of the participants went bust, and the average payout was just $91. Only 21% of the participants reached the maximum. 18 of the 61 participants bet everything on one toss, while two-thirds gambled on tails at some stage in the experiment." Using the Kelly criterion and based on the odds in the experiment, the right approach would be to bet 20% of the pot on each throw (see first example in Statement below). If losing, the size of the bet gets cut; if winning, the stake increases.
nibble  betting  investing  ORFE  acm  checklists  levers  probability  algorithms  wiki  reference  atoms  extrema  parsimony  tidbits  decision-theory  decision-making  street-fighting  mental-math  calculation 
august 2017 by nhaliday
Stat 260/CS 294: Bayesian Modeling and Inference
- Priors (conjugate, noninformative, reference)
- Hierarchical models, spatial models, longitudinal models, dynamic models, survival models
- Testing
- Model choice
- Inference (importance sampling, MCMC, sequential Monte Carlo)
- Nonparametric models (Dirichlet processes, Gaussian processes, neutral-to-the-right processes, completely random measures)
- Decision theory and frequentist perspectives (complete class theorems, consistency, empirical Bayes)
- Experimental design
unit  course  berkeley  expert  michael-jordan  machine-learning  acm  bayesian  probability  stats  lecture-notes  priors-posteriors  markov  monte-carlo  frequentist  latent-variables  decision-theory  expert-experience  confidence  sampling 
july 2017 by nhaliday

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