An analysis of the implicit choice set generation using the Constrained Multinomial Logit model

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We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how ...

Environmental impact objectives are commonly found in building performance labels and rating schemes. Anticipating a building’s impact from the conceptual design stage and identifying decisions that do not compromise its chances of reaching these targets i ...

In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms s ...

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivat ...

The brain has been theorized to employ inferential processes to overcome the problem of uncertainty. Inference is thought to underlie neural processes, including in disparate domains such as value-based decision-making and perception. Value-based decision- ...

Neighborhood form and building facade design are key drivers of indoor daylight performance. Architectural design at the neighborhood scale starts with massing scheme proposals and other design specifications including façade design are typically enriched ...

We study stochastic bilevel programs where the leader chooses a binary here-and-now decision and the follower responds with a continuous wait-and-see-decision. Using modern decision rule approximations, we construct lower bounds on an optimistic version an ...

Stochastic programming and distributionally robust optimization seek deterministic decisions that optimize a risk measure, possibly in view of the most adverse distribution in an ambiguity set. We investigate under which circumstances such deterministic de ...

An analysis of the implicit choice set generation using the Constrained Multinomial Logit model

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