Second best toll pricing within the framework of bounded rationality

Chapter 5 - Understanding rational inefficiency: A scientific basis for economic failure and success

Previous studies of transportation network design problem (NDP) always consider one peak-hour origin-destination (O-D) demand distribution. However, the NDP based on one peak-hour O-D demand matrix might be unable to model the real traffic patterns due to diverse traffic characteristics in the morning and evening peaks and impacts of network structure and link sensitivity. This paper proposes an NDP model simultaneously considering both morning and evening peak-hour demands. The NDP problem is formulated as a bilevel programming model, where the upper level is to minimize the weighted sum of total travel time for network users travelling in both morning and evening commute peaks, and the lower level is to characterize user equilibrium choice behaviors of the travelers in two peaks. The proposed NDP model is transformed into an equivalent mixed integer linear programming (MILP), which can be efficiently solved by optimization solvers. Numerical examples are finally performed to demonstrate the effectiveness of the developed model. It is shown that the proposed NDP model has more promising design effect of improving network efficiency than the traditional NDP model considering one peak-hour demand and avoids the misleading selection of improved links.

This paper will examine how pragmatism has created rational choice behavior in candidate selection. Pragmatism has resulted in single candidates for local heads, which makes local democracy uncompetitive. The research method used is an explanatory approach that aims to explain how pragmatism as a rational choice behavior in the local government head election. Data were collected, compiled and analyzed using Nvivo 12 Plus software. The results showed that political parties as political institutions are very pragmatic in the selection of candidates for local heads. The incumbents utilized the support of almost all political parties based on clientelistic and programmatic relations. This has led the candidate selection process to prioritize pragmatic aspects, especially financial support, acceptability, and electability of candidates. The candidate selection process no longer determines ideological values at the official candidate selection process. Financial support for candidates has the highest priority for political parties in determining their endorsements in regional head elections. Meanwhile, the acceptability aspect refers to the dominant acceptance of all elites, cadres, and the mass base of supporters of the selected candidate. Electability is also a priority, although it is considered that electability trends are dynamic and able to change based on the level of competition. The behavior of political party elite actors and local head candidates eventually forms a rational choice pattern that makes local democracy backsliding.

Existing second best toll pricing (SBTP) models determine optimal tolls of a subset of links in a transportation network by minimizing certain system objective, while the traffic flow pattern is assumed to follow user equilibrium (UE). We show in this paper that such toll design approach is risk prone, which tries to optimize for the best-case scenario, if the UE problem has multiple solutions. Accordingly, we propose a risk averse SBTP approach aiming to optimize for the worst-case scenario, which can be formulated as a min-max problem. We establish a general solution existence condition for the risk averse model and discuss in detail that such a condition may not be always satisfied in reality. In case a solution does not exist, it is possible to replace the exact UE solution set by a set of approximate solutions. This replacement guarantees the solution existence of the risk averse model. We then develop a scheme such that the solution set of an affine UE can be explicitly expressed. Using this explicit representation, an improved simplex method can be adopted to solve the risk averse SBTP model.

Large-scale multimodal transportation network models and algorithms-Part II: Network capacity and network design problem

Rationality in decision making is commonly assessed by comparing choice performance against normative standards. We argue that such a performance-centered approach blurs the distinction between rational choice and adaptive behavior. Instead, rational choice should be assessed with regard to the way individuals make analytic decisions. We suggest that analytic decisions can be made in two different modes in which control processes are directed at different levels. In a RUN mode, thought is directed at controlling the operation of a decision strategy. In the JUMP mode, the individual analyses the interpretation of the decision situation as well as the appropriateness of alternative strategies. We suggest that a decision should be considered “rational” when an individual is able to switch between these modes and balance their interaction.

In this paper we study constraint qualifications and optimality conditions for bilevel programming problems. We strive to derive checkable constraint qualifications in terms of problem data and applicable optimality conditions. For the bilevel program with convex lower level program we discuss drawbacks of reformulating a bilevel programming problem by the mathematical program with complementarity constraints and present a new sharp necessary optimality condition for the reformulation by the mathematical program with a generalized equation constraint. For the bilevel program with a nonconvex lower level program we propose a relaxed constant positive linear dependence (RCPLD) condition for the combined program.

PurposeHousing choice is always a complicated decision with its dual functions as a roof over the head and as an investment good. This paper aims to investigate the boundedly rational behaviours that affect the housing choice three bounded behaviours play roles in explaining the decision-making behaviour of homebuyers when they acquire/sell a property. These behaviours are endowment effect, loss aversion and herding, which have implications on the decision-making process.Design/methodology/approachThe research is based on cross-sectional questionnaires and collected from 587 respondents. Factor analysis and reliability tests were used to identify the latent construct of bounded rational housing choice behaviour. In the meantime, the study used one-way analysis of variance (ANOVA) to examine whether there are any differences in the housing choice based on the respondents’ demographic backgrounds.FindingsThe findings indicated that a total of 11 items were reduced to three factors that accounted for the decision-making in housing choice. There are significant differences in herding behaviour amongst respondents with different level of education and their purpose of looking for a house.Research limitations/implicationsThis paper helps to identify latent constructs that shed light on the housing choice, especially on the bounded rational behaviour.Originality/valueThis is one of the few studies to explore boundedly rational behaviours in housing choice from the angle of homebuyers. Previous studies addressed housing choice in terms of price, demand and supply in general but not on individual homebuyers. The results will be useful to developers, policymakers, homebuyers as well as scholars in understanding the decision-making process in housing choice.

Electronic countermeasure (ECM) has become one of the most significant factors in modern warfare, in the course of combat, the electronic jamming allocation tasks need to be flexibly adjusted with the change of combat stage, which puts forward higher requirements for the modeling and solution method of this kind of problems. To solve the ECM dynamic weapon target assignment (ECM-DWTA) problem, a hybrid multi-target bi-level programming model is established. The upper level takes the sum of the electronic jamming effects in the whole combat stage as an optimization objective, and locally optimizes the ECM weapon (ECM-WP) assignment scheme in each stage. The lower level takes the importance expectation value of the target subjected to interference and combat consumption as double optimization objectives to globally optimize the ECM-WP assignment scheme. Focus on solving this complex model, a hybrid multi-objective bi-level interactive fuzzy programming algorithm (HMOBIF) is proposed, in this method, exponential membership function is used to describe the satisfaction degree of each level. When solving the multi-objective optimization problem composed of membership functions in the upper and lower levels, we use the MOEA/D algorithm to obtain the Pareto Front (PF) solution set, and then each solution in PF is evaluated and selected by the TOPSIS multi-criteria evaluation method. This local and global interactive optimization process of bi-level model is actually the process of executing observation-orientation-decision-action loop in practical combat. According to the current example, we conduct numerical simulation on the parameters in the model and obtain the parameter values suitable for the model solution. The computational experiments on different scale ECM-DWTA problems show that HMOBIF method is superior to four bi-level programming algorithms in terms of performance index, and can better solve ECM-DWTA problems.

By handling the travel cost function artfully, the authors formulate the transportation mixed network design problem (MNDP) as a mixed-integer, nonlinear bilevel programming problem, in which the lower-level problem, comparing with that of conventional bilevel DNDP models, is not a side constrained user equilibrium assignment problem, but a standard user equilibrium assignment problem. Then, the bilevel programming model for MNDP is reformulated as a continuous version of bilevel programming problem by the continuation method. By virtue of the optimal-value function, the lower-level assignment problem can be expressed as a nonlinear equality constraint. Therefore, the bilevel programming model for MNDP can be transformed into an equivalent single-level optimization problem. By exploring the inherent nature of the MNDP, the optimal-value function for the lower-level equilibrium assignment problem is proved to be continuously differentiable and its functional value and gradient can be obtained efficiently. Thus, a continuously differentiable but still nonconvex optimization formulation of the MNDP is created, and then a locally convergent algorithm is proposed by applying penalty function method. The inner loop of solving the subproblem is mainly to implement an all-or-nothing assignment. Finally, a small-scale transportation network and a large-scale network are presented to verify the proposed model and algorithm.

This paper addresses the road toll pricing and capacity investment problem in a congested road network in a multicriteria decisionmaking framework. A goal programming approach is used in which the following four major goals are considered: (1) cost recovery, (2) service level, (3) environmental, and (4) equity. The multiobjective road toll pricing and capacity investment problem is formulated as a bilevel goal programming model. The upper level of the model aims to minimize the deviations from stated goals for a given priority ranking of the goals, while the lower level is the road users' route choice equilibrium problem. A simulated annealing-based solution algorithm is developed to solve the proposed model. Numerical results show that the priority structure of the goals can significantly affect the road toll pricing and capacity investment decisions. The simulated annealing-based solution algorithm outperforms the sensitivity analysis-based solution algorithm in terms of solution quality. The proposed methodology provides an avenue for understanding the trade-offs among conflicting objectives and for designing a financially and environmentally sustainable transportation system.

The standard random utility model assumes a fully rational and compensatory choice behaviour. However, various research studies have proven that non-compensatory /semi-compensatory choice behaviour is more realistic. This paper proposes a semi-compensatory framework for the discrete choice model that combines probabilistic choice set formation along with implicit choice constraints in choice making. It combines the Independent Availability Logit (IAL) with Implicit Constrained Multinomial Logit model (CMNL). The model is applied to investigate the mode choice behaviour of the Ottawa-Gatineau regions of Canada’s capital by using the household travel survey data. The explanatory power and elasticity measures of the proposed model are compared with IAL and MNL models. It is found that the IAL-CMNL model outperforms both IAL and MNL models and can reproduce the choice set formation process more effectively. The empirical investigation shows that the IAL-CMNL model results in relatively higher tolerance and softer constraints for cut-off violations compared to the IAL model. Elasticity calculations and outcomes of this research highlight the importance of capturing choice set formation and constrained choice behaviour in the choice modelling.

A bi-level programming model is used to describe the continuous equilibrium network design problem in this paper. Traffic demand and supply that are both random are described to be, respectively, truncated normal distribution and logarithmic normal distribution. At the upper level model, the optimization objective is to minimize the mean of total impedance of network system and enhancement of road capacity as decision-making variables. The constrained condition is investment budget. The lower level program is formulated as a user equilibrium traffic assignment model, which link flow defined as the decision-making variable. Above all, a Monte Carlo simulation-based genetic algorithm was provided to solve the bi-level model and a satisfied result on the calculation and analysis of a small tested road network can be had.

<p style='text-indent:20px;'>This paper studies the stability for bilevel program where the lower-level program is a multiobjective programming problem. As we know, the weakly efficient solution mapping for parametric multiobjective program is not generally lower semicontinuous. We first obtain this semicontinuity under a suitable assumption. Then, a new condition for the lower semicontinuity of the efficient solution mapping of this problem is also obtained. Finally, we get the continuities of the value functions and the solution set mapping for the upper-level problem based on the semicontinuities of solution mappings for the lower-level parametric multiobjective program.</p>

Bilevel optimization formulation for parameter estimation in vapor–liquid(–liquid) phase equilibrium problems