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  • Institute for Operations Research and the Management Sciences (INFORMS)  (3)
  • Economics  (3)
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  • Institute for Operations Research and the Management Sciences (INFORMS)  (3)
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  • Economics  (3)
RVK
  • 1
    Online Resource
    Online Resource
    Reference-Point Formation and Updating
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2011
    In:  Management Science Vol. 57, No. 3 ( 2011-03), p. 506-519
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 57, No. 3 ( 2011-03), p. 506-519
    Abstract: Reference-dependent preferences have been well accepted in decision sciences, experimental economics, behavioral finance, and marketing. However, we still know very little about how decision makers form and update their reference points given a sequence of information. Our paper provides some novel experiments in a financial context to advance the understanding of reference-point formation over time. Our subjects' reference price is best described as a combination of the first and the last price of the time series, with intermediate prices receiving smaller and nondecaying weights. Hence, reference prices are not recursive. We provide a parsimonious formula to predict the reference points, which we test out-of-sample. The fit of the model is reasonably good. This paper was accepted by George Wu, decision analysis.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.1100.1286
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2011
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Choice-Based Elicitation and Decomposition of Decision Weights for Gains and Losses Under Uncertainty
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2005
    In:  Management Science Vol. 51, No. 9 ( 2005-09), p. 1384-1399
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 51, No. 9 ( 2005-09), p. 1384-1399
    Abstract: This paper reports the results of an experimental parameter-free elicitation and decomposition of decision weights under uncertainty. Assuming cumulative prospect theory, utility functions were elicited for gains and losses at an individual level using the tradeoff method. Subsequently, decision weights were elicited through certainty equivalents of uncertain two-outcome prospects. Furthermore, decision weights were decomposed using observable choice instead of invoking other empirical primitives, as in previous experimental studies. The choice-based elicitation of decision weights allows for a quantitative study of their characteristics, and also allows, among other things, for the examination of the sign-dependence hypothesis for observed choice under uncertainty. Our results confirm concavity of the utility function in the gain domain and bounded subadditivity of decision weights and choice-based subjective probabilities. We also find evidence for sign dependence of decision weights.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.1050.0388
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2005
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    On the Selection of the Globally Optimal Prototype Subset for Nearest-Neighbor Classification
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2007
    In:  INFORMS Journal on Computing Vol. 19, No. 3 ( 2007-08), p. 470-479
    Details
    In: INFORMS Journal on Computing, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 19, No. 3 ( 2007-08), p. 470-479
    Abstract: The nearest-neighbor classifier has been shown to be a powerful tool for multiclass classification. We explore both theoretical properties and empirical behavior of a variant method, in which the nearest-neighbor rule is applied to a reduced set of prototypes. This set is selected a priori by fixing its cardinality and minimizing the empirical misclassification cost. In this way we alleviate the two serious drawbacks of the nearest-neighbor method: high storage requirements and time-consuming queries. Finding this reduced set is shown to be NP-hard. We provide mixed integer programming (MIP) formulations, which are theoretically compared and solved by a standard MIP solver for small problem instances. We show that the classifiers derived from these formulations are comparable to benchmark procedures. We solve large problem instances by a metaheuristic that yields good classification rules in reasonable time. Additional experiments indicate that prototype-based nearest-neighbor classifiers remain quite stable in the presence of missing values.
    Type of Medium: Online Resource
    ISSN: 1091-9856 , 1526-5528
    URL: Article
    DOI: 10.1287/ijoc.1060.0183
    RVK:
    QA 10000
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2007
    detail.hit.zdb_id: 2070411-2
    detail.hit.zdb_id: 2004082-9
    SSG: 3,2
    Location Call Number Limitation Availability
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    Online Resource
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