Hideyuki Mizuta

Agent-based simulation of dynamic online auctions

The need to understand dynamic behavior in auctions is increasing with the popularization of online auctions. Applications include designing auction mechanisms, bidding strategies, and server systems. We describe simulations of a typical online auction, where the duration is fixed, and the second-highest price is continuously posted and determines the winners payment. We modeled agents of exactly two types, idealizations and simplifications of those observed in practice: early bidders, who can bid any time during the auction period, and snipers, who wait till the last moments to bid. This allows us to study the interactions of the two types of bidders during the course of auctions, and the effects of the two strategies on the probability of winning, the final price, and the formation of price consensus in iterated auctions. Results show that: 1) early bidders can win with a lower price on average than snipers, but much less often; 2) the late bidding strategy of snipers is effective; and 3) in iterated auctions, adjustment feedback of motivational parameters can lead to effective price consensus with small fluctuations.

Dot pattern generation technique using molecular dynamics

We have developed a new technique for generating homogeneously distributed irregular dot patterns useful for optical devices and digital halftoning technologies. To introduce irregularity, we use elaborately designed sequences called low-discrepancy sequences instead of pseudorandom numbers. We also use a molecular-dynamics redistribution method to improve the distribution of dots. Our method can produce arbitrary density distributions in accordance with a given design. The generated patterns are free from visible roughness as well as any moiré patterns when superimposed on other regular patterns. We demonstrate that our method effectively improves luminance uniformity and eliminates moiré patterns when used for a backlight unit of a liquid-crystal display.

Agent-based simulation and greenhouse gas emissions trading

The need for new theoretical and experimental approaches to understand dynamic and heterogeneous behavior in complex economic and social systems is increasing. An approach using agent-based simulation and artificial markets on a computer system is considered to be an effective approach. Computational simulation with dynamically interacting heterogeneous agents is expected to re-produce complex phenomena in economics, and helps us to experiment various controlling methods, evaluate systematic designs, and extract the fundamental elements which produce interesting phenomena for future analytical work. In previous work, we investigated the stability of a virtual commodities market and the aggregated behavior of dynamic online auctions with heterogeneous agents. In this paper, we introduce a simple framework to develop agent-based simulations systematically and consider an application of agent-based simulation for a dynamical model of international greenhouse gas emissions trading.

Effects of price signal choices on market stability

Using simulation and analysis we show that agent-based auction-cleared automated markets can be stabilized using only completely myopic agents (without value traders), if these na¨ ove agents are provided with a price signal that reflects order book information. This demonstrates that information in the order book is extremely valuable, that prediction can be replaced by better instantaneous information about others’ bids, and suggests new, more stable algorithms for market-based control.

Toward simulating entire cities with behavioral models of traffic

Resilient transportation systems enable quick evacuation, rescue, distribution of relief supplies, and other activities for reducing the impact of natural disasters and for accelerating the recovery from them. The resilience of a transportation system largely relies on the decisions made during a natural disaster. We developed an agent-based traffic simulator for predicting the results of potential actions taken with respect to the transportation system to quickly make appropriate decisions. For realistic simulation, we govern the behavior of individual drivers of vehicles with foundational principles learned from probe-car data. For example, we used the probe-car data to estimate the personality of individual drivers of vehicles in selecting their routes, taking into account various metrics of routes such as travel time, travel distance, and the number of turns. This behavioral model, which was constructed from actual data, constitutes a special feature of our simulator. We built this simulator using the X10 language, which enables massively parallel execution for simulating traffic in a large metropolitan area. We report the use cases of the simulator in three major cities in the context of disaster recovery and resilient transportation.

A novel dot‐pattern generation to improve luminance uniformity of LCD backlight

We report on a novel theoretical approach to generate irregular dot patterns, providing an integrated solution to difficulties peculiar to collimated -type backlight units. By applying this technology to a light guide and a diffuser film, the

Agent-based simulation of CO2 emissions trading and large-scale traffic flow

The first commitment period of the Kyoto Protocols has started this year to prevent the global warming. There are many efforts and discussions on effective greenhouse gas (GHG) reduction. The emissions trading is one of important issues. Several experimental markets are starting by governments and private sectors. However, there are some opinions against the trading itself. Also, the estimation of trading amount and market price is difficult with such a huge and emerging market. In this paper, we introduce the international emissions trading with the agent-based approach and a scalable simulation framework applied to large-scale traffic flow.

45.2: Moiré-Free Collimating Light Guide with Low-Discrepancy Dot Patterns

the notion of discrepancy, we developed a new technique for generating uniformly distributed dot patterns. We designed and prototyped a light guide having prismatic grooves and micro scatterers on the surface. We experimentally confirmed that the new pattern of the micro scatterers effectively prevents moire patterns, and improves the luminance homogeneity.

Agent-Based Simulation for Economic and Environmental Studies

The need for new theoretical and experimental approaches to understand dynamic and heterogeneous behavior in complex economic and social systems is increasing. Computational simulation with dynamically interacting heterogeneous agents is expected to be able to reproduce complex phenomena in economics, and helps us to experiment with various controlling methods, to evaluate systematic designs, and to extract the fundamental elements which produce the interesting phenomena in depth analysis. To implement various applications of the agent-based simulation effectively, we have developed a simple framework. We also consider a new application of agent-based simulation for an environmental study and implement a preliminary simulation model of the international greenhouse gas (GHG) emissions trading.

Collective Response Spike Prediction for Mutually Interacting Consumers

Modeling how marketing actions in various channels influence or cause consumer purchase decisions is crucial for marketing decision-making. Marketing campaigns stimulate consumer awareness, interest and help drive interactions such as the browsing of product web pages, ultimately impacting an individuals purchase decision. In addition, some successful campaigns stimulate word-of-mouth and social trends among consumers, and such collective behavior of consumers result in concurrent and correlated responses over a short term. Though each consumers response should be attributed with both the same individuals experiences and the collective factors, unobservability of most word-of-mouth events makes the estimation challenging. The authors propose a new continuous-time predictive model for time-dependent response rates of each consumer, which can incorporate both the individual and the collective factors without explicit word-of-mouth observations. The individual factor is modeled as staircase functions associated with the experienced events by each consumer, and provides a clear psychological interpretation about how marketing advertising communications impact short-term and mid-term memories of consumers. The collective factor is modeled with aggregate response frequencies for mutually-interacting groups that are automatically estimated from data. The key idea to mine the mutually-interacting groups exists in a three-step estimator, which initially performs a Poisson regression without the collective factor, then does clustering of the residual time-series in the initial regression, and finally performs another Poisson regression involving the collective factor. The proposed collective factor robustly incorporates the underlying trends even when causality from one consumers event spikes to another consumers response is weak. High predictive accuracy of the proposed approach is empirically validated using real-world data provided by an online retailer in Europe.