Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents

National Sweatfree Summit 2010

This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.ILRF_NationalSweatfreeSummit2010Notes.pdf: 96 downloads, before Oct. 1, 2020

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management

Assessment of Adaptability of a Supply Chain Trading Agent’s Strategy: Evolutionary Game Theory Approach

With the increase in the complexity of supply chain management, the use of intelligent agents for automated trading has gained popularity (Collins, Arunachalam, B, et al. 2006). The performance of supply-chain agents depends on not just the market environment (supply and demand patterns) but also on what types of other agents they are competing with. For designers of such agents it is important to ascertain that their agents are robust and can adapt to changing market and competitive environments. However, to date there has not been any work done that assesses the adaptability of a trading agent’s strategy in the presence of various demand and supply distributions when competing with a changing composition of agents using different strategies. In this paper we use the concept of replicator dynamics to study the evolution of a population of strategies used by supply chain agents when the different agents are competing against each other. We also study the evolution of the population of agents’ strategies in the presence of six types of adverse market conditions. In particular we test three strategies that have been presented in the literature and our results indicate that over time supply chain agents gravitate towards using the SCMaster strategy in most scenarios

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

Deep Reinforcement Learning for Supply Chain Synchronization

Supply chain synchronization can prevent the “bullwhip effect” and significantly mitigate ripple effects caused by operational failures. This paper demonstrates how deep reinforcement learning agents based on the proximal policy optimization algorithm can synchronize inbound and outbound flows if end-toend visibility is provided. The paper concludes that the proposed solution has the potential to perform adaptive control in complex supply chains. Furthermore, the proposed approach is general, task unspecific, and adaptive in the sense that prior knowledge about the system is not required

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Spatial competition of learning agents in agricultural procurement markets

Spatially dispersed farmers supply raw milk as the primary input to a small number of large dairy-processing firms. The spatial competition of processing firms has short- to long-term repercussions on farm and processor structure, as it determines the regional demand for raw milk and the resulting raw milk price. A number of recent analytical and empirical contributions in the literature analyse the spatial price competition of processing firms in milk markets. Agent-based models (ABMs) serve by now as computational laboratories in many social science and interdisciplinary fields and are recently also introduced as bottom-up approaches to help understand market outcomes emerging from autonomously deciding and interacting agents. Despite ABMs' strengths, the inclusion of interactive learning by intelligent agents is not sufficiently matured. Although the literature of multi-agent systems (MASs) and multi-agent economic simulation are related fields of research they have progressed along separate paths. This thesis takes us through some basic steps involved in developing a theoretical basis for designing multi-agent learning in spatial economic ABMs. Each of the three main chapters of the thesis investigates a core issue for designing interactive learning systems with the overarching aim of better understanding the emergence of pricing behaviour in real, spatial agricultural markets. An important problem in the competitive spatial economics literature is the lack of a rigorous theoretical explanation for observed collusive behavior in oligopsonistic markets. The first main chapter theoretically derives how the incorporation of foresight in agents' pricing policy in spatial markets might move the system towards cooperative Nash equilibria. It is shown that a basic level of foresight invites competing firms to cease limitless price wars. Introducing the concept of an outside option into the agents' decisions within a dynamic pricing game reveals viihow decreasing returns for increasing strategic thinking correlates with the relevance of transportation costs. In the second main chapter, we introduce a new learning algorithm for rational agents using H-PHC (hierarchical policy hill climbing) in spatial markets. While MASs algorithms are typically just applicable to small problems, we show experimentally how a community of multiple rational agents is able to overcome the coordination problem in a variety of spatial (and non-spatial) market games of rich decision spaces with modest computational effort. The theoretical explanation of emerging price equilibria in spatial markets is much disputed in the literature. The majority of papers attribute the pricing behavior of processing firms (mill price and freight absorption) merely to the spatial structure of markets. Based on a computational approach with interactive learning agents in two-dimensional space, the third main chapter suggests that associating the extent of freight absorption just with the factor space can be ambiguous. In addition, the pricing behavior of agricultural processors – namely the ability to coordinate and achieve mutually beneficial outcomes - also depends on their ability to learn from each other