Search

Our journal Systems (https://www [...]

Many problems faced by decision makers today involve the management of large scale, complex systems that can be modeled as state-based control problems, specifically discrete Markov decision process (MDP). Typical examples include transportation systems, defense systems, healthcare networks, financial organizations, and general infrastructure problems. In all of these problems, decision makers have difficulty in forecasting the state of their system in the future and capturing the dynamics of the states over time. In this paper, we discuss, via numerous examples, practical experiences in trying to build such models. Much of the literature discusses theoretical issues of solution convergence and algorithm performance; unfortunately, much of this research does not help with the practical business of building an actual MDP model. Thus, numerous books begin with statement of the nature: "given the state space S. . .." A critical question to the practitioner is the creation of this state space "S." We focus on this first step in the MDP modeling process, an often neglected and difficult step, and we discuss the practical implications and issues associated with the state definition, illustrating these issues with numerous examples. This paper is not meant to be a survey of "state-based" applications or MDP applications, but an overview of experiences building many of these models in diverse applications.

This paper presents an innovative new approach to investment portfolio design, which applies a discrete, state-based methodology to defining market states and making asset allocation decisions with respect to both current and future state membership. State membership is based on attributes taken from traditional finance and portfolio theory namely expected growth, and covariance. The transitional dynamics of the derived states are modeled as a Markovian process. Asset weighting and portfolio allocation decisions are made through an optimization-based approach coupled with heuristics that account for the probability of state membership and the quality of the state in terms of information provided.

Quantifiable, measurable risk is of critical importance when making data-driven decisions in finance and investment management, but what if the generally accepted practice of the investment industry for calculating risk possessed incorrect mathematical assumptions and embedded biases? This piece revisits the discussion surrounding the methodology used to calculate annualized standard deviation statistics commonly used when reporting the performance of investment products. It goes on to present a new example illustrating the bias when applied to an efficient frontier.

Market participants often invoke the concept of discrete state when discussing financial markets. Bull market, bear market, depression, and recession are all terms that map to discrete market states. Mental models of how markets behave in each state and transition between states are then applied to decision-making. Implicit to that approach is the assumption that states are persistent and recurrent over time. This article seeks to formalize notions of discrete market states by proposing a parsimonious and innovative approach to segmenting periods of time into discrete states. The technique is demonstrated and evaluated in a series of case studies.

Traffic congestion is a worsening problem in metropolitan areas which will require integrated regional traffic control systems to improve traffic conditions. This paper presents a regional traffic control system which can detect incident conditions and provide integrated traffic management during nonrecurrent congestion events. The system combines advanced artificial intelligence techniques with a traffic performance model based on HCM equations. Preliminary evaluation of the control system using traffic microsimulation demonstrates that it has the potential to improve system conditions during traffic incidents. In addition, several enhancements were identified which will make the system more robust in a real traffic control setting. An assessment of the control system elements indicates that there are no substantial technical barriers in implementing this system in a large traffic network.