This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. This second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

We propose a model of preferences in which the effect of randomization on ambiguity depends on how the unknown probability law is determined. We adopt the framework of Anscombe and Aumann (1963) and relax the axioms. In the resulting representation of the individual's preference, the individual has a collection of sets of priors M . She believes that before she moves, nature has chosen an unknown scenario (a set of priors) from M , and from that scenario, nature will choose a prior after she moves. The representation illustrates how randomization may partially eliminate the effect of ambiguity.

The most critical issue in evaluating policies and projects that affect generations of individuals is the choice of social discount rate. This paper shows that there exist social discount rates such that the planner can simultaneously be (i) an exponential discounting expected utility maximizer; (ii) intergenerationally Pareto—that is, if all individuals from all generations prefer one policy/project to another, the planner agrees; and (iii) strongly non‐dictatorial—that is, no individual from any generation is ignored. Moreover, to satisfy (i)–(iii), if the time horizon is long enough, it is generically sufficient and necessary for social discounting to be more patient than the most patient individual's long‐run discounting, independent of the social risk attitude.

To compare the efficacy and safety of treatments based on the Stupp protocol for adult patients with newly diagnosed glioblastoma and to determine the optimal treatment option for patients with different O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation statuses. We estimated hazard ratios (HRs) for overall survival (OS) and odds ratios (ORs) for adverse events of grade 3 or higher (AEs ≥ 3). Twenty-one randomized controlled trials involving 6478 patients treated with 21 different treatment strategies were included. Results of the pooled HRs indicated tumor-treating fields (TTF) combined with the Stupp protocol resulted in the most favorable OS for patients with and without MGMT promoter methylation. Subgroup analyses by the two MGMT promoter statuses indicated that lomustinetemozolomide plus radiotherapy or TTF combination therapy was associated with the best OS for patients with methylated MGMT promoter (HR, 1.03; 95% credible interval [CI], 0.54-1.97), and standard cilengitide combination therapy or TTF combination treatment was associated with the best OS for patients with unmethylated MGMT promoter (HR, 1.05; 95% CI, 0.67-1.64). Regarding AEs ≥ 3, there were no significant differences in pooled ORs. However, Bayesian ranking profiles that demonstrated intensive cilengitide combination therapy and TTF combination therapy have a similar possibility to cause the least toxicity. These results indicated that TTF combination therapy was associated with increased survival, irrespective of the MGMT promoter methylation status, and a relatively tolerated safety profile compared with other combination treatments. The optimal treatment option for glioblastoma patients with different MGMT promoter methylation statuses was different.

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