The Normalization of Consumer Valuations: Context-Dependent Preferences From Neurobiological Constraints

2020 ◽  
Author(s):  
Ryan Webb ◽  
Paul W. Glimcher ◽  
Kenway Louie
Keyword(s):  
Decision Making ◽  
Discrete Choice ◽  
Choice Model ◽  
Judgment And Decision Making ◽  
Choice Behaviour ◽  
Divisive Normalization ◽  
Choice Sets ◽  
Observed Behaviour ◽  
The Brain ◽  
Choice Set

Consumer valuations are shaped by choice sets, exemplified by patterns of substitution between alternatives as choice sets are varied. Building on recent neuroeconomic evidence that valuations are transformed during the choice process, we incorporate the canonical divisive normalization computation into a discrete choice model and characterize how choice behaviour depends on both size and composition of the choice set. We then examine evidence for such behaviour from two choice experiments that vary the size and composition of the choice set. We find that divisive normalization more accurately captures observed behaviour than alternative models, including an example range normalization model. These results are robust across experimental paradigms. Finally, we demonstrate that Divisive Normalization implements an efficient means for the brain to represent valuations given neurobiological constraints, yielding the fewest choice errors possible given those constraints. This paper was accepted by Elke Weber, judgment and decision making.

scholarly journals Multi-route choice modelling in a metropolitan context: A comparative analysis using Multinomial Logit and Fuzzy Logic based approaches

2020 ◽  
Vol 79 (ET.2020) ◽  
pp. 1-17
Author(s):  
Sowjanya Dhulipala
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Choice Model ◽  
Route Choice ◽  
Fuzzy Rule ◽  
Multinomial Logit ◽  
Choice Modelling ◽  
Choice Behaviour ◽  
Linguistic Rules ◽  
Mnl Model

Route choice plays a vital role in the traffic assignment and network building, as it involves decision making on part of riders. The vagueness in travellers’ perceptions of attributes of the available routes between any two locations adds to the complexities in modelling the route choice behaviour. Conventional Logit models fail to address the uncertainty in travellers’ perceptions of route characteristics (especially qualitative attributes, such as environmental effects), which can be better addressed through the theory of fuzzy sets and linguistic variables. This study thus attempts to model travellers’ route choice behaviour, using a fuzzy logic approach that is based on simple and logical ‘if-then’ linguistic rules. This approach takes into consideration the uncertainty in travellers’ perceptions of route characteristics, resembling humans’ decision-making process. Three attributes – travel time, traffic congestion, and road-side environment are adopted as factors driving people’s choice of routes, and three alternative routes between two typical locations in an Indian metropolitan city, Surat, are considered in the study. The approach to deal with multiple routes is shown by analyzing two-wheeler riders’ (e.g. motorcyclists’ and scooter drivers’) route choice behaviour during the peak-traffic time. Further, a Multinomial Logit (MNL) model is estimated, to enable a comparison of the two modelling approaches. The estimated Fuzzy Rule-Based Route Choice Model outperformed the conventional MNL model, accounting for the uncertain behaviour of travellers.

Design of Discrete Choice Experiments

2019 ◽  
Author(s):  
Deborah J. Street ◽  
Rosalie Viney
Keyword(s):  
Discrete Choice ◽  
Choice Experiment ◽  
Stated Preference ◽  
Choice Experiments ◽  
Individual Characteristics ◽  
Discrete Choice Experiments ◽  
Random Component ◽  
Choice Sets ◽  
Attribute Levels ◽  
Choice Set

Discrete choice experiments are a popular stated preference tool in health economics and have been used to address policy questions, establish consumer preferences for health and healthcare, and value health states, among other applications. They are particularly useful when revealed preference data are not available. Most commonly in choice experiments respondents are presented with a situation in which a choice must be made and with a a set of possible options. The options are described by a number of attributes, each of which takes a particular level for each option. The set of possible options is called a “choice set,” and a set of choice sets comprises the choice experiment. The attributes and levels are chosen by the analyst to allow modeling of the underlying preferences of respondents. Respondents are assumed to make utility-maximizing decisions, and the goal of the choice experiment is to estimate how the attribute levels affect the utility of the individual. Utility is assumed to have a systematic component (related to the attributes and levels) and a random component (which may relate to unobserved determinants of utility, individual characteristics or random variation in choices), and an assumption must be made about the distribution of the random component. The structure of the set of choice sets, from the universe of possible choice sets represented by the attributes and levels, that is shown to respondents determines which models can be fitted to the observed choice data and how accurately the effect of the attribute levels can be estimated. Important structural issues include the number of options in each choice set and whether or not options in the same choice set have common attribute levels. Two broad approaches to constructing the set of choice sets that make up a DCE exist—theoretical and algorithmic—and no consensus exists about which approach consistently delivers better designs, although simulation studies and in-field comparisons of designs constructed by both approaches exist.

The Effect of Dynamic and Static Choice Sets on Political Decision Making: An Analysis Using the Decision Board Platform

1997 ◽  
Vol 91 (3) ◽  
pp. 553-566 ◽  
Author(s):  
Alex Mintz ◽  
Nehemia Geva ◽  
Steven B. Redd ◽  
Amy Carnes
Keyword(s):  
Decision Making ◽  
Decision Process ◽  
A Priori ◽  
Decision Makers ◽  
Strategy Selection ◽  
Political Decision ◽  
Choice Sets ◽  
Static Versus Dynamic ◽  
Political Decision Making ◽  
Choice Set

Previous studies of political decision making have used only “static” choice sets, where alternatives are “fixed” and are a priori known to the decision maker. We assess the effect of a dynamic choice set (new alternatives appear during the decision process) on strategy selection and choice in international politics. We suggest that decision makers use a mixture of decision strategies when making decisions in a two-stage process consisting of an initial screening of available alternatives, and a selection of the best one from the subset of remaining alternatives. To test the effects of dynamic and static choice sets on the decision process we introduce a computer-based “process tracer” in a study of top-ranking officers in the U.S. Air Force. The results show that (1) national security decision makers use a mixture of strategies in arriving at a decision, and (2) strategy selection and choice are significantly influenced by the structure of the choice set (static versus dynamic).

scholarly journals Exploring Route Choice Behaviours Accommodating Stochastic Choice Set Generations

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shin-Hyung Cho ◽  
Seung-Young Kho
Keyword(s):  
Radio Frequency Identification ◽  
Choice Model ◽  
Route Choice ◽  
Revealed Preference ◽  
Guidance System ◽  
Model Parameters ◽  
Generation Model ◽  
Choice Behaviour ◽  
Choice Set Generation ◽  
Choice Set

Modelling route choice behaviours are essential in traffic operation and transportation planning. Many studies have focused on route choice behaviour using the stochastic model, and they have tried to construct the heterogeneous route choice model with various types of data. This study aims to develop the route choice model incorporating travellers’ heterogeneity according to the stochastic route choice set. The model is evaluated from the empirical travel data based on a radio frequency identification device (RFID) called dedicated short-range communication (DSRC). The reliability level is defined to explore the travellers’ heterogeneity in the choice set generation model. The heterogeneous K-reliable shortest path- (HK α RSP-) based route choice model is established to incorporate travellers’ heterogeneity in route choice behaviour. The model parameters are estimated for the mixed path-size correction logit (MPSCL) model, considering the overlapping paths and the heterogeneous behaviour in the route choice model. The different behaviours concerning the chosen routes are analysed to interpret the route choice behaviour from revealed preference data by comparing the different coefficients’ magnitude. There are model validation processes to confirm the prediction accuracy according to travel distance. This study discusses the policy implication to introduce the traveller specified route travel guidance system.

The Poliheuristic Theory of Crisis Decision Making and Applied Decision Analysis

2021 ◽  
Author(s):  
Inbal Hakman ◽  
Alex Mintz ◽  
Steven B. Redd
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
High Stress ◽  
Decision Makers ◽  
Decision Task ◽  
Two Stage ◽  
Second Stage ◽  
Choice Sets ◽  
Stage Process ◽  
Choice Set

Poliheuristic theory addresses the “why” and “how” of decision making. It focuses on how decision makers use heuristics en route to choice by addressing both the process and the choice related to the decision task. More specifically, decision makers use a two-stage process wherein a more complicated choice set is reduced to one that is more manageable through the use of these heuristics, or cognitive shortcuts. In the second stage, decision makers are more likely to employ maximizing and analytical strategies in making a choice. Poliheuristic theory also focuses on the political consequences of decision making, arguing that decision makers will refrain from making politically costly decisions. While poliheuristic theory helps us better understand how decision makers process information and make choices, it does not specifically address how choice sets and decision matrices were created in the first place. Applied decision analysis (ADA) rectifies this shortcoming by focusing on how leaders create particular choice sets and matrices and then how they arrive at a choice. It does so by first identifying the decision maker’s choice set or decision matrix; that is, the alternatives or options available to choose from as well as the criteria or dimensions upon which the options will be evaluated. ADA then focuses on uncovering the decision maker’s decision code through the use of multiple decision models. Combining poliheuristic theory with ADA allows researchers to more fully explain decision making in general and crisis decision making in particular. An application of poliheuristic theory and ADA to decision making pertaining to the Fukushima nuclear disaster reveals that even in this high-stress crisis environment decision makers followed the two-stage process as predicted by poliheuristic theory. More specifically, in the first stage, decision makers simplified the decision task by resorting to cognitive heuristics (i.e., decision making shortcuts) to eliminate politically damaging alternatives such as voluntary evacuation. In the second stage, decision makers conducted a more analytical evaluation of the compulsory evacuation options.

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