Decision Rules

2021 ◽  
pp. 32-41
Author(s):  
Charles E. Phelps ◽  
Guru Madhavan

Group decisions are driven by rules—constitutions, bylaws, contracts. Often the set of choices voted on by the group has been winnowed down by a committee or a backroom process that can strongly control the outcome by determining what choices are offered (and how they are described). This prescreening is often filled with obscure rules and processes. Organizations that come to crucial decision points (sometimes vital to the organization’s future) may find themselves suddenly looking at their bylaws (or whatever controls these processes) to find out how things should be done, but when those rules are poorly constructed (or give immense power to a few select people within the group), bad decisions can emerge that please very few people. The time to review organizational bylaws and rules is before crucial votes appear, not in the midst of major decisions themselves.

Biostatistics ◽  
2015 ◽  
Vol 17 (1) ◽  
pp. 135-148 ◽  
Author(s):  
Ashkan Ertefaie ◽  
Tianshuang Wu ◽  
Kevin G. Lynch ◽  
Inbal Nahum-Shani

Abstract A dynamic treatment regime (DTR) is a treatment design that seeks to accommodate patient heterogeneity in response to treatment. DTRs can be operationalized by a sequence of decision rules that map patient information to treatment options at specific decision points. The sequential, multiple assignment, randomized trial (SMART) is a trial design that was developed specifically for the purpose of obtaining data that informs the construction of good (i.e. efficacious) decision rules. One of the scientific questions motivating a SMART concerns the comparison of multiple DTRs that are embedded in the design. Typical approaches for identifying the best DTRs involve all possible comparisons between DTRs that are embedded in a SMART, at the cost of greatly reduced power to the extent that the number of embedded DTRs (EDTRs) increase. Here, we propose a method that will enable investigators to use SMART study data more efficiently to identify the set that contains the most efficacious EDTRs. Our method ensures that the true best EDTRs are included in this set with at least a given probability. Simulation results are presented to evaluate the proposed method, and the Extending Treatment Effectiveness of Naltrexone SMART study data are analyzed to illustrate its application.


2004 ◽  
Author(s):  
Kevin D. Carlson ◽  
Mary L. Connerley ◽  
Arlise P. McKinney ◽  
Ross L. Mecham

2008 ◽  
Author(s):  
Michel Handgraaf ◽  
Philip Schuette ◽  
Nicole Yoskowitz ◽  
Elke Weber ◽  
Kerry Milch ◽  
...  
Keyword(s):  

2002 ◽  
Vol 2 (3) ◽  
pp. 135-141
Author(s):  
Susan L. Hendrix ◽  
Richard Derman ◽  
Richard T. Kloos
Keyword(s):  

Author(s):  
Michael Laver ◽  
Ernest Sergenti

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.


Sign in / Sign up

Export Citation Format

Share Document