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Searching far away from the lamp-post: An agent-based model

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Searching far away from the lamp-post : An agent-based model. / Vuculescu, Oana.

I: Strategic Organization, Bind 15, Nr. 2, 2017, s. 242-263.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Vuculescu, Oana. / Searching far away from the lamp-post : An agent-based model. I: Strategic Organization. 2017 ; Bind 15, Nr. 2. s. 242-263.

Bibtex

@article{95a99aa4ebaf406ba31fd3caf795bfb3,
title = "Searching far away from the lamp-post: An agent-based model",
abstract = "This article presents insights from a laboratory experiment on human problem solving in a combinatorial task. I rely on a hierarchical rugged landscape to explore how human problem-solvers are able to detect and exploit patterns in their search for an optimal solution. Empirical findings suggest that solvers do not engage only in local and random distant search, but as they accumulate information about the problem structure, solvers make {\textquoteleft}model-based{\textquoteright} moves, a type of cognitive search. I then calibrate an agent-based model of search to analyse and interpret the findings from the experimental setup and discuss implications for organizational search. Simulation results show that, for non-trivial problems, performance can be increased by a low level of persistence, that is, an increased likelihood to quickly abandon unsuccessful paths.",
author = "Oana Vuculescu",
year = "2017",
doi = "10.1177/1476127016669869",
language = "English",
volume = "15",
pages = "242--263",
journal = "Strategic Organization",
issn = "1476-1270",
publisher = "SAGE Publications Ltd",
number = "2",

}

RIS

TY - JOUR

T1 - Searching far away from the lamp-post

T2 - An agent-based model

AU - Vuculescu, Oana

PY - 2017

Y1 - 2017

N2 - This article presents insights from a laboratory experiment on human problem solving in a combinatorial task. I rely on a hierarchical rugged landscape to explore how human problem-solvers are able to detect and exploit patterns in their search for an optimal solution. Empirical findings suggest that solvers do not engage only in local and random distant search, but as they accumulate information about the problem structure, solvers make ‘model-based’ moves, a type of cognitive search. I then calibrate an agent-based model of search to analyse and interpret the findings from the experimental setup and discuss implications for organizational search. Simulation results show that, for non-trivial problems, performance can be increased by a low level of persistence, that is, an increased likelihood to quickly abandon unsuccessful paths.

AB - This article presents insights from a laboratory experiment on human problem solving in a combinatorial task. I rely on a hierarchical rugged landscape to explore how human problem-solvers are able to detect and exploit patterns in their search for an optimal solution. Empirical findings suggest that solvers do not engage only in local and random distant search, but as they accumulate information about the problem structure, solvers make ‘model-based’ moves, a type of cognitive search. I then calibrate an agent-based model of search to analyse and interpret the findings from the experimental setup and discuss implications for organizational search. Simulation results show that, for non-trivial problems, performance can be increased by a low level of persistence, that is, an increased likelihood to quickly abandon unsuccessful paths.

U2 - 10.1177/1476127016669869

DO - 10.1177/1476127016669869

M3 - Journal article

VL - 15

SP - 242

EP - 263

JO - Strategic Organization

JF - Strategic Organization

SN - 1476-1270

IS - 2

ER -