Searched for: subject%3A%22Decision%255C+rule%22
(1 - 11 of 11)
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van Cranenburgh, S. (author), Meyerhoff, Jürgen (author), Rehdanz, Katrin (author), Wunsch, Andrea (author)
Efficient experimental designs aim to maximise the information obtained from stated choice data to estimate discrete choice models' parameters statistically efficiently. Almost without exception efficient experimental designs assume that decision-makers use a Random Utility Maximisation (RUM) decision rule. When using such designs,...
journal article 2024
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Liebe, Ulf (author), van Cranenburgh, S. (author), Chorus, C.G. (author)
Empirical studies on individual behaviour often, implicitly or explicitly, assume a single type of decision rule. Other studies do not specify behavioural assumptions at all. We advance sociological research by introducing (random) regret minimization, which is related to loss aversion, into the sociological literature and by testing it against ...
journal article 2023
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Geržinič, N. (author), van Cranenburgh, S. (author), Cats, O. (author), Lancsar, Emily (author), Chorus, C.G. (author)
Since the introduction of Discrete Choice Analysis, countless efforts have been made to enhance the efficiency of data collection through choice experiments and to improve the behavioural realism of choice models. One example development in data collection are best-worst discrete choice experiments (BWDCE), which have the benefit of obtaining...
journal article 2021
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Caunhye, Aakil M. (author), Aydin, N.Y. (author), Duzgun, H. Sebnem (author)
Route restoration is considered to be a task of foremost priority in disaster relief. In this paper, we propose a robust optimization approach for post-disaster route restoration under uncertain restoration times. We present a novel decision rule based on restoration time ordering that yields optimal restoration sequencing and propose...
journal article 2020
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van Cranenburgh, S. (author), Alwosheel, A.S.A. (author)
This study develops a novel Artificial Neural Network (ANN) based approach to investigate decision rule heterogeneity amongst travellers. This complements earlier work on decision rule heterogeneity based on Latent Class discrete choice models. We train our ANN to recognise the choice patterns of four distinct decision rules: Random Utility...
journal article 2019
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van Cranenburgh, S. (author), Collins, Andrew T. (author)
At the time of creating an experimental design for a stated choice experiment, the analyst often does not precisely know which model, or decision rule, he or she will estimate once the data are collected. This paper presents two new software tools for creating stated choice experimental designs that are simultaneously efficient for regret...
journal article 2019
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van Cranenburgh, S. (author), Chorus, C.G. (author)
This paper is the first to study to what extent decision rules, embedded in disaggregate discrete choice models, matter for large-scale aggregate level mobility forecasts. Such large-scale forecasts are a crucial underpinning for many transport infrastructure investment decisions. We show, in the particular context of (linear-additive)...
journal article 2018
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van Cranenburgh, S. (author), Prato, Carlo G. (author), Chorus, C.G. (author)
This paper derives a trick to account for variation in choice set size in Random Regret Minimization (RRM) models. In many choice situations the choice set size varies across choice observations. As in RRM models regret level differences increase with increasing choice set size, not accounting for variation in choice set size results in RRM...
working paper 2015
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Van Middelkoop, M. (author), Boumeester, H.J.F.M. (author)
Quantitative theories and approaches have been dominant in a variety of disciplines. In this contribution, we will explore an alternative approach: decision tables, i.c. sets of decision rules extracted from an existing data set using a CHAID-based algorithm, that focus on conditions and states leading to particular actions or decisions. These...
report 2014
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Amiri-Simkooei, A.R. (author), Snellen, M. (author), Simons, D.G. (author)
In a recent work described in Ref. [1], an angle-independent methodology was developed to use the multi-beam echo sounder backscatter (MBES) data for the seabed sediment classification. The method employs the backscatter data at a certain angle to obtain the number of sediment classes and to discriminate between them by applying the Bayes...
conference paper 2009
document
Loog, M. (author)
journal article 2008
Searched for: subject%3A%22Decision%255C+rule%22
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