An optimal sequential information acquisition model subject to a heuristic assimilation constraint
Purpose – The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set. Design/methodology/approach –...
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Published in | Benchmarking : an international journal Vol. 23; no. 4; pp. 937 - 982 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Bradford
Emerald Group Publishing Limited
03.05.2016
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Abstract | Purpose
– The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set.
Design/methodology/approach
– The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained.
Findings
– The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs.
Originality/value
– The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered. |
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AbstractList | Purpose
– The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set.
Design/methodology/approach
– The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained.
Findings
– The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs.
Originality/value
– The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered. Purpose – The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set. Design/methodology/approach – The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained. Findings – The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs. Originality/value – The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered. |
Author | Tavana, Madjid Di Caprio, Debora Santos-Arteaga, Francisco J |
Author_xml | – sequence: 1 givenname: Debora surname: Di Caprio fullname: Di Caprio, Debora organization: Department of Mathematics and Statistics, York University, Toronto, Canada – sequence: 2 givenname: Francisco J surname: Santos-Arteaga fullname: Santos-Arteaga, Francisco J organization: Departamento de Economía Aplicada II, Universidad Complutense de Madri, Madrid, Spain – sequence: 3 givenname: Madjid surname: Tavana fullname: Tavana, Madjid organization: Business Systems and Analytics Department, La Salle University, Philadelphia, Pennsylvania, USA |
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Cites_doi | 10.1080/07421222.1998.11518212 10.1287/opre.1080.0611 10.1287/opre.8.3.362 10.1287/mnsc.10.4.746 10.1111/1468-0394.00195 10.1111/j.1468-0394.2010.00573.x 10.1007/978-94-015-7815-8_4 10.1007/978-0-387-77131-1_4 10.1086/591910 10.1504/IJOR.2014.061770 10.1016/S0305-0548(03)00074-1 10.1016/j.inffus.2012.10.005 10.1207/S15327663JCP13-1&2_07 10.1207/s15327663jcp1403_6 10.1287/mnsc.37.11.1474 10.1016/0378-7206(94)90048-5 10.1007/s11750-009-0114-3 10.1504/IJADS.2009.026549 10.1016/0167-9236(86)90001-1 10.1016/0165-1889(94)00854-7 10.4324/9780203496039 10.1016/S1566-2535(02)00102-1 10.1287/opre.1080.0534 10.1016/0167-9236(87)90035-2 10.1287/mnsc.31.11.1372 10.1016/S1566-2535(03)00033-2 10.1287/deca.1050.0044 10.1016/j.inffus.2008.05.002 10.2307/1912183 10.1016/j.inffus.2010.09.001 10.1016/j.jmateco.2011.07.006 10.1017/CBO9780511803475 10.1016/j.eswa.2008.01.021 10.1016/j.obhdp.2006.10.006 10.1109/69.634747 |
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SubjectTerms | Acquisitions & mergers Algorithms Assimilation Benchmarking Computer simulation Constraints Data processing Decision making Dynamic programming Economic models Expected utility Expected values Heuristic Mathematical analysis Mathematical models Money markets Operations research Optimization Organizational performance Strategy Utility functions |
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