scholarly journals Robust sequential search

2021 ◽  
Vol 16 (4) ◽  
pp. 1431-1470
Author(s):  
Karl H. Schlag ◽  
Andriy Zapechelnyuk
Keyword(s):  
Decision Rules ◽  
Sequential Search ◽  
Option Value ◽  
Outside Option ◽  
Independent And Identically Distributed

We study sequential search without priors. Our interest lies in decision rules that are close to being optimal under each prior and after each history. We call these rules robust. The search literature employs optimal rules based on cutoff strategies, and these rules are not robust. We derive robust rules and show that their performance exceeds 1/2 of the optimum against binary independent and identically distributed (i.i.d.) environments and 1/4 of the optimum against all i.i.d. environments. This performance improves substantially with the outside option value; for instance, it exceeds 2/3 of the optimum if the outside option exceeds 1/6 of the highest possible alternative.

Perceptions of Group Decision Rules and Group Decisions

2001 ◽  
Author(s):  
Charles E. Miller ◽  
Yohsuke Ohtsubo
Keyword(s):  
Group Decision ◽  
Decision Rules ◽  
Group Decisions

Six GPA Decision Rules and Adverse Impact in Initial Screening

2004 ◽  
Author(s):  
Kevin D. Carlson ◽  
Mary L. Connerley ◽  
Arlise P. McKinney ◽  
Ross L. Mecham
Keyword(s):  
Decision Rules ◽  
Adverse Impact ◽  
Initial Screening

Decision Rules and the Transformation of Perceptual Information Into Decision-Related Action

2011 ◽  
Author(s):  
Corey N. White ◽  
Russell A. Poldrack
Keyword(s):  
Decision Rules ◽  
Perceptual Information

Stopping Rules in a Sequential Search Task

1965 ◽  
Author(s):  
Amnon Rapoport ◽  
James Kahan
Keyword(s):  
Search Task ◽  
Stopping Rules ◽  
Sequential Search

Adequate Decision Rules for Portfolio Choice Problems

2002 ◽  
Author(s):  
Thilo Goodall
Keyword(s):  
Portfolio Choice ◽  
Decision Rules

The Evolutionary Dynamics of Decision Rule Selection

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Rule ◽  
Positive Feedback ◽  
Party System ◽  
Evolutionary Dynamics ◽  
Decision Rules ◽  
Past Performance ◽  
Party Leaders ◽  
Track Record ◽  
Long Run ◽  
Survival Of The Fittest

This chapter extends the survival-of-the-fittest evolutionary environment to consider the possibility that new political parties, when they first come into existence, do not pick decision rules at random but instead choose rules that have a track record of past success. This is done by adding replicator-mutator dynamics to the model, according to which the probability that each rule is selected by a new party is an evolving but noisy function of that rule's past performance. Estimating characteristic outputs when this type of positive feedback enters the dynamic model creates new methodological challenges. The simulation results show that it is very rare for one decision rule to drive out all others over the long run. While the diversity of decision rules used by party leaders is drastically reduced with such positive feedback in the party system, and while some particular decision rule is typically prominent over a certain period of time, party systems in which party leaders use different decision rules are sustained over substantial periods.

Endogenous Parties, Interaction of Different Decision Rules

2017 ◽  
Author(s):  
Michael Laver ◽  
Ernest Sergenti
Keyword(s):  
Decision Rule ◽  
Decision Rules ◽  
Party Competition ◽  
Competitive Environment ◽  
Party Leaders ◽  
Robert Axelrod

This chapter attempts to develop more realistic and interesting models in which the set of competing parties is a completely endogenous output of the process of party competition. It also seeks to model party competition when different party leaders use different decision rules in the same setting by building on an approach pioneered in a different context by Robert Axelrod. This involves long-running computer “tournaments” that allow investigation of the performance and “robustness” of decision rules in an environment where any politician using any rule may encounter an opponent using either the same decision rule or some quite different rule. The chapter is most interested in how a decision rule performs against anything the competitive environment might throw against it, including agents using decision rules that are difficult to anticipate and/or comprehend.

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