Header

UZH-Logo

Maintenance Infos

Strategy Selection Versus Strategy Blending: A Predictive Perspective on Single- and Multi-Strategy Accounts in Multiple-Cue Estimation


Herzog, Stefan M; von Helversen, Bettina (2018). Strategy Selection Versus Strategy Blending: A Predictive Perspective on Single- and Multi-Strategy Accounts in Multiple-Cue Estimation. Journal of Behavioral Decision Making, 31(2):233-249.

Abstract

The claim that a person can use different strategies or processes to solve the same task is pervasive in decision making, categorization, estimation, reasoning, and other research fields. Yet such multi-strategy approaches differ widely in how they envision that the different strategies are coordinated and therefore do not represent one unitary approach. Toolbox models, for example, assume that people shift from one strategy to another as they adapt to specific task environments based on past experience. Unlike such multi-strategy selection approaches, multi-strategy blending approaches assume that the outputs of different strategies are blended into a joint, hybrid response (i.e., “wisdom of strategies” in one mind). The goal of this article is twofold. First, we discuss strategy blending as a conceptual alternative to strategy selection for modeling human judgment. Second, we investigate the predictive performance of the different approaches in synthetic and real-world environments. Taking a normative perspective, we study the coordination of rule-based and exemplar-based processes in estimation tasks. Our simulations using synthetic and real-world environments indicate that, for medium-sized samples, multi-strategy blending approaches lead to more accurate estimates than relying on a single strategy or selecting a strategy based on past experience—possibly because neither rule- nor exemplar-based processes in isolation are sufficient to capture statistical regularities that enable accurate estimates. This suggests that multi-strategy blending approaches can be advantageous to the degree that they rely on qualitatively different strategies.

Abstract

The claim that a person can use different strategies or processes to solve the same task is pervasive in decision making, categorization, estimation, reasoning, and other research fields. Yet such multi-strategy approaches differ widely in how they envision that the different strategies are coordinated and therefore do not represent one unitary approach. Toolbox models, for example, assume that people shift from one strategy to another as they adapt to specific task environments based on past experience. Unlike such multi-strategy selection approaches, multi-strategy blending approaches assume that the outputs of different strategies are blended into a joint, hybrid response (i.e., “wisdom of strategies” in one mind). The goal of this article is twofold. First, we discuss strategy blending as a conceptual alternative to strategy selection for modeling human judgment. Second, we investigate the predictive performance of the different approaches in synthetic and real-world environments. Taking a normative perspective, we study the coordination of rule-based and exemplar-based processes in estimation tasks. Our simulations using synthetic and real-world environments indicate that, for medium-sized samples, multi-strategy blending approaches lead to more accurate estimates than relying on a single strategy or selecting a strategy based on past experience—possibly because neither rule- nor exemplar-based processes in isolation are sufficient to capture statistical regularities that enable accurate estimates. This suggests that multi-strategy blending approaches can be advantageous to the degree that they rely on qualitatively different strategies.

Statistics

Citations

Dimensions.ai Metrics
1 citation in Web of Science®
2 citations in Scopus®
2 citations in Microsoft Academic
Google Scholar™

Altmetrics

Downloads

0 downloads since deposited on 03 Mar 2017
8 downloads since 12 months

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Language:English
Date:2018
Deposited On:03 Mar 2017 10:53
Last Modified:28 Jul 2018 05:19
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0894-3257
Additional Information:This is the peer reviewed version of the following article: Herzog, Stefan M; von Helversen, Bettina (2018). Strategy Selection Versus Strategy Blending: A Predictive Perspective on Single- and Multi-Strategy Accounts in Multiple-Cue Estimation. Journal of Behavioral Decision Making, 31(2):233-249, which has been published in final form at https://doi.org/10.1002/bdm.1958. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms).
OA Status:Green
Publisher DOI:https://doi.org/10.1002/bdm.1958
Project Information:
  • : FunderSNSF
  • : Grant ID100014_146169
  • : Project TitleModeling Human Judgment: Integrating Memory and Rule-based Processes

Download

Download PDF  'Strategy Selection Versus Strategy Blending: A Predictive Perspective on Single- and Multi-Strategy Accounts in Multiple-Cue Estimation'.
Preview
Content: Accepted Version
Filetype: PDF
Size: 842kB
View at publisher