PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICS

We designed an economic task to investigate human planning of routes in landscapes where travel in different kinds of terrain incurs different costs. Participants moved their finger across a touch screen from a starting point to a destination. The screen was divided into distinct kinds of terrain an...

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Main Authors: Hang eZhang, Soumya V Maddula, Laurence T Maloney
Format: Article
Language:English
Published: Frontiers Media S.A. 2010-12-01
Series:Frontiers in Psychology
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fpsyg.2010.00214/full
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spelling doaj-f84525b2f4de4ddd9248316397072b6d2020-11-24T22:12:59ZengFrontiers Media S.A.Frontiers in Psychology1664-10782010-12-01110.3389/fpsyg.2010.002147155PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICSHang eZhang0Hang eZhang1Soumya V Maddula2Laurence T Maloney3Laurence T Maloney4New York UniversityNew York UniversityNew York UniversityNew York UniversityNew York UniversityWe designed an economic task to investigate human planning of routes in landscapes where travel in different kinds of terrain incurs different costs. Participants moved their finger across a touch screen from a starting point to a destination. The screen was divided into distinct kinds of terrain and travel within each kind of terrain imposed a cost proportional to distance traveled. We varied costs and spatial configurations of terrains and participants received fixed bonuses minus the total cost of the routes they chose. We first compared performance to a model maximizing gain. All but one of 12 participants failed to adopt least-cost routes and their failure to do so reduced their winnings by about 30% (median value). We tested in detail whether participants’ choices of routes satisfied three necessary conditions (heuristics) for a route to maximize gain. We report failures of one heuristic for 7 out of 12 participants. Last of all, we modeled human performance with the assumption that participants assign subjective utilities to costs and maximize utility. For 7 out 12 participants, the fitted utility function was an accelerating power function of actual cost and for the remaining 5, a decelerating power function. We discuss connections between utility aggregation in route planning and decision under risk. Our task could be adapted to investigate human strategy and optimality of route planning in full-scale landscapes.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2010.00214/fullDecision MakingnavigationBayesian decision theoryHeuristicsoptimalityroute selection
collection DOAJ
language English
format Article
sources DOAJ
author Hang eZhang
Hang eZhang
Soumya V Maddula
Laurence T Maloney
Laurence T Maloney
spellingShingle Hang eZhang
Hang eZhang
Soumya V Maddula
Laurence T Maloney
Laurence T Maloney
PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICS
Frontiers in Psychology
Decision Making
navigation
Bayesian decision theory
Heuristics
optimality
route selection
author_facet Hang eZhang
Hang eZhang
Soumya V Maddula
Laurence T Maloney
Laurence T Maloney
author_sort Hang eZhang
title PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICS
title_short PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICS
title_full PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICS
title_fullStr PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICS
title_full_unstemmed PLANNING ROUTES ACROSS ECONOMIC TERRAINS: MAXIMIZING UTILITY, FOLLOWING HEURISTICS
title_sort planning routes across economic terrains: maximizing utility, following heuristics
publisher Frontiers Media S.A.
series Frontiers in Psychology
issn 1664-1078
publishDate 2010-12-01
description We designed an economic task to investigate human planning of routes in landscapes where travel in different kinds of terrain incurs different costs. Participants moved their finger across a touch screen from a starting point to a destination. The screen was divided into distinct kinds of terrain and travel within each kind of terrain imposed a cost proportional to distance traveled. We varied costs and spatial configurations of terrains and participants received fixed bonuses minus the total cost of the routes they chose. We first compared performance to a model maximizing gain. All but one of 12 participants failed to adopt least-cost routes and their failure to do so reduced their winnings by about 30% (median value). We tested in detail whether participants’ choices of routes satisfied three necessary conditions (heuristics) for a route to maximize gain. We report failures of one heuristic for 7 out of 12 participants. Last of all, we modeled human performance with the assumption that participants assign subjective utilities to costs and maximize utility. For 7 out 12 participants, the fitted utility function was an accelerating power function of actual cost and for the remaining 5, a decelerating power function. We discuss connections between utility aggregation in route planning and decision under risk. Our task could be adapted to investigate human strategy and optimality of route planning in full-scale landscapes.
topic Decision Making
navigation
Bayesian decision theory
Heuristics
optimality
route selection
url http://journal.frontiersin.org/Journal/10.3389/fpsyg.2010.00214/full
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