Toshihiko Miyagi

A Spatial Computable General Equilibrium Approach for Measuring Multiregional Impacts of Large Scale Transportation Projects

In economically lagging regions, infrastructure, transportation infrastructure in particular, is seen as an essential prerequisite for economic development. Even in industrialized regions, where the existing infrastructure’s performance is deteriorating due to increasing inadequacy to cope with enlarging travel demand or superannuated transportation systems, there is still interest in the effects of new or improved transportation infrastructure. At the same time, governments are concerned with the cost of providing and maintaining infrastructure when there are pressures to reduce public expenditure. This requires governments or planners to give a rigorous explanation about the needs of the infrastructure that, in turn, implies a need for more accurate assessment of the incidence of the wider economic benefits from the infrastructure.

The entropy production function and its application to the multi-commodity and multi-sector model

Although the entropy function has been widely used in regional science, its underlying economic implications do not seem to have been explored in a significant way. The main purpose of this paper is an attempt to reconcile entropy model approaches with production theory in economics. In order to do so, we start with an appropriate assumption about cross-price elasticities of the input demand function of individual firms, and show that the existence of variations in price elasticities among demanded commodities induces heterogeneous production behavior. As a result, we can have an entropic form of the production function. This type of production function is first developed for a single output firm, then extended to include multi-output and multi-input production industries.

A COMPUTATIONAL PROCEDURE FOR DETERMINATION OF OLIGOPOLISTIC SPATIAL PRICE EQUILIBRIUM

The primary purpose of this paper is to develop a computational procedure for finding the Cournot equilibrium for a case in which many firms located at different points in space compete in many different markets located at different points in space. The model investigated here allows for the fact that not all firms sell to all regions. The algorithm is first developed under the assumptions (a) that the inverse demand function in each market is convex, and (b) that production costs of each firm are composed of a fixed cost and a constant marginal cost. It is shown that the proposed method with the convex combination algorithm is applicable to a spatial pricing model with variable marginal-cost functions.

Adaptive Learning Algorithms for Simulation-Based Dynamic Traffic User Equilibrium

We propose two classes of algorithms for achieving the user equilibrium in simulation-based dynamic traffic assignment with special attention given to the interactions between travel information and route choice behavior. A driver is assumed to perform day-to-day route choice repeatedly and experience payoffs with unknown noise. The driver adaptively changes his/her action in accordance with the payoffs that are initially unknown and must be estimated over time due to noisy observations. To solve this problem, we develop a multi-agent version of Q-learning to estimate the payoff functions using novel forms of the ε−greedy learning policy. We apply this Q-learning scheme for the simulation-based DTA in which traffic flow and travel times of routes in the traffic network are generated by a microscopic traffic simulator based on cellular automaton. Finally, we provide simulation examples to show convergence of our algorithms to Nash equilibrium and effectiveness of the best-route provision services.