Bgc Benedict Dellaert

Modeling and measuring individuals’mental representations of complex spatio-temporal decision problems

Based on mental model theory, we expect individuals to construct a mental representation of the system they interact with which tends to be a strong reduction of reality and is tailored to the specific situation and task at hand. Such reductions may be particularly significant in complex decision situations involved in local spatial choice behavior. In this article, we develop a method to model and measure mental representations of decision problems involving individual spatio-temporal choice behavior in different situations. The so-called CNET method consists of an interview protocol to elicit the structures at the individual level as a causal network. We test the proposed method in a case study involving 180 respondents and an experimental shopping-trip planning task. The results indicate that the method is an adequate way of eliciting mental representations. We show how the networks revealed can be used to model and simulate reasoning and decision-making processes.

Tourists’ Mental Representations of Complex Travel Decision Problems

Tourism research has long recognized the complexity of many decisions that tourists make and proposed models to describe and analyze tourist decision processes. This article complements this previous research by proposing a view that moves away from the process of making a decision and instead investigates how tourists mentally represent the decision problem. Mental representations of travel decision problems allow tourists to evaluate the decision utility of the different available alternatives before they are chosen. We discuss the modeling and measurement of tourists’ mental representations of complex travel decision problems and provide directions for future research that focuses on the dynamics of tourist mental representations both within a single travel decision framework and over the tourist travel life cycle.

Integrating Prospect Theory and Stackelberg Games to Model Strategic Dyad Behavior of Information Providers and Travelers: Theory and Numerical Simulations

Existing policy models of optimal guidance strategies are typically concerned with single-objective optimization based on reliable forecasts in terms of the consistency between predicted and observed aggregate activity-travel patterns. The interaction and interdependencies between policy objective and individuals have not received much attention. This paper considers how one specific activity schedule choice—namely, the start time of an activity chosen by individual travelers under guidance information—aggregates to form an equilibrium distribution, which in turn influences guidance generation and determines the best possible achievement of the policy objective. These choices are formalized as the outcomes of a Stackelberg game in which a travelers behavior model is integrated with prospect theory. The properties of the model are examined by using numerical computer simulations. The results of the simulations support the face validity of the formulated model.

Modeling Impact of Emerging Uncertain Time-Dependent Aggregate Activity-Travel Patterns on Individual Activity Participation and Timing Decisions

Existing activity-based models are typically concerned with predicting observed aggregate activity-travel patterns. The interaction and dependencies between individuals have received less attention. Considered here is how one specific schedule choice, namely, the start time of an activity chosen by an agent, aggregates to form an equilibrium distribution, which in turn influences individual decision making. These choices are formalized as the outcomes of a game of incomplete information in which agents have to conjecture the behavior of other agents. The properties of the model are examined by using numerical computer simulations. The results of the simulations support the face validity of the formulated model.

Using Conjoint Choice Experiments to Model Consumer Choices of Product Component Packages

Recent advances in flexibility and automation allow a growing number of manufacturers and service providers to ‘mass-customize’ their products and offer modules from which consumers can create their own individualized products (e.g., Gilmore and Pine 1997). Traditional production processes limit consumer choices to fixed products defined by suppliers, but new mass-customization processes allow consumers to create their own optimal combination of product components. Although mass-customization offers consumers increased flexibility and consumption utility, little is known about how consumer choices to package or bundle separate components differ (if at all) from choices among traditional fixed product options, much less what the impact of packaging product components will be on the market shares of such products or a producer’s overall share in the category.