Hajime Mizuyama

A Prediction Market System for Aggregating Dispersed Tacit Knowledge into a Continuous Forecasted Demand Distribution

This research proposes a novel demand forecasting method which will work effectively even in such circumstances where extrapolate-able demand patterns are hardly available. The method uses the market mechanism to aggregate tacit knowledge of the firm’s sales people on the future demand of a product into a continuous forecasted demand distribution. In order to make it work, the paper introduces a new type of prediction security and an original market maker algorithm suitable for the security, and furnishes them into an intra-firm prediction market system. As a result, sufficient liquidity is secured for the market even when the number of the traders is small, and the market maker can output at any time an aggregated demand forecast of the traders as a continuous distribution. An agent simulation model, where each trader has the log-utility function, is also developed to show how the method works, how quickly the output distribution converges, etc.

A prediction market system using SIPS and generalized LMSR for collective-knowledge-based demand forecasting

This paper proposes and verifies a prediction market system for obtaining a forecast distribution of the demand quantity for a certain product in a future time period based on the collective knowledge of employees or customers themselves of a company. It first introduces a new type of prediction securities called the self-adjustable interval prediction securities (SIPS); SIPS are a set of winner-takes-all type contracts and each of which is tied to the future event that the actual demand quantity is included in a certain interval. The prediction intervals are mutually exclusive and collectively cover the whole feasible region of the demand quantity. What is unique to SIPS is that how the whole feasible region is partitioned into a set of prediction intervals is dynamically and adaptively self-adjusted so as to improve the accuracy of the output forecast distribution. It next generalizes the logarithmic market scoring rule (LMSR), which is one of the most popular market making algorithms for a prediction market, to SIPS. The paper also tests how the proposed prediction market system works in a laboratory setting and confirms that SIPS outperform the fixed interval prediction securities (FIPS) in terms of the accuracy of the output forecast distribution.

Experimental Evaluation of Inventory-Based Discrete-Updating Market Maker for Intra-firm Prediction Market System Using VIPS

This paper develops an intra-firm prediction market system as a collective-knowledge-based forecasting tool for a company and evaluates its performance through laboratory experiments. The system uses the variable-interval prediction security (VIPS) as the prediction security to be traded in the market and is controlled by an original computerized market maker suitable for the security type. The market maker evaluates each unit of VIPS with a Gaussian price distribution and updates the distribution intermittently through an inventory-based updating logic according to the transactions in the market. Laboratory experiments are conducted with a virtual demand forecasting problem to study whether the system functions properly as a subjective forecasting tool. The experiments confirm that the system is capable of penalizing arbitrage actions and hence its performance is fairly stable. Further, the output price distribution can serve as an approximate forecast distribution.

A semiotic characterization of the process of teaching and learning a skilled motion taking wok handling as an example.

Abstract How to teach and learn a skilled motion efficiently has long been a crucial issue in various fields. This research pays attention to the fact that receiving advice from an instructor often accelerates the process of mastering a skilled motion, and thus assumes that a set of advice given to a learner functions as a trigger that introduces a structural change into how the learner performs the motion. This leads to a state transition model of the process of learning a skilled motion with advice. Taking a basic motion of wok handling as an example, this paper observes several actual processes of learning it with advice and captures and visualizes them according to the model. It further characterizes and compares the processes based on the sequence of advice and how the motion performance changes along it. As a result, it becomes obvious that the process differs among learners and that the instructor determines the set of advice to give based not simply on the observable characteristics of the latest motion performance but more strategically upon the history of the interaction with the learner.

Design and Simulation-Based Testing of a Prediction Market System Using SIPS for Demand Forecasting

Self-adjustable interval prediction securities (SIPS) are newly proposed prediction securities that are suitable for market-based demand forecasting. The whole feasible region of the demand quantity to be estimated is divided into a fixed number of mutually exclusive and collectively exhaustive prediction intervals. Subsequently, a set of winner-take-all-type securities are issued that correspond to these intervals. Each portion of the securities wins a unitary payoff only if the actual sales volume falls in the corresponding interval. The contracts are called SIPS because the borders between the intervals are dynamically and adaptively self-adjusted to maintain the informativeness of the output forecast distribution. This paper first designs a prediction market system using SIPS equipped with a central market maker and then confirms how the system operates through agent-based simulation.

How Innovation and Improvement Ideas Are Created for a Production System by a Kaizen Team: A Protocol Analysis

This paper presents a process model describing how a Kaizen team collectively creates innovation and improvement ideas for a production system and a protocol analysis approach based on the model. The process model captures the team-based creative problem solving practice as a process of updating a shared mental space comprising production system mental models (PSMMs) through exchanging utterances among the team members, where each utterance element characterizes an existing PSMM and/or creates a new PSMM. The paper also applies the proposed protocol analysis approach to an actual case, confirms its applicability and draws some insights into the process.