1,459 research outputs found
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
Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes
Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain
Transparency as Delayed Observability in Multi-Agent Systems
Is transparency always beneficial in complex systems such as traffic networks
and stock markets? How is transparency defined in multi-agent systems, and what
is its optimal degree at which social welfare is highest? We take an
agent-based view to define transparency (or its lacking) as delay in agent
observability of environment states, and utilize simulations to analyze the
impact of delay on social welfare. To model the adaptation of agent strategies
with varying delays, we model agents as learners maximizing the same objectives
under different delays in a simulated environment. Focusing on two agent types
- constrained and unconstrained, we use multi-agent reinforcement learning to
evaluate the impact of delay on agent outcomes and social welfare. Empirical
demonstration of our framework in simulated financial markets shows opposing
trends in outcomes of the constrained and unconstrained agents with delay, with
an optimal partial transparency regime at which social welfare is maximal
Intelligent Agent Based Approach to Sales Operations at eStores
In our paper, we consider the application of Intelligent Agents in supporting the operations in an Internet-based store (estore). We consider and discuss different opportunities for employing Intelligent Agents to improve the performance of an estore\u27s operations, including sales, forecasting demand and supporting order fulfillment. We provide a framework for the application of such agents, show available sources of information, and discuss challenging issues in modeling learning and decision processes for agents
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
An investigation of the trading agent competition : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand
The Internet has swept over the whole world. It is influencing almost every aspect of society. The blooming of electronic commerce on the back of the Internet further increases globalisation and free trade. However, the Internet will never reach its full potential as a new electronic media or marketplace unless agents are developed. The trading Agent Competition (TAC), which simulates online auctions, was designed to create a standard problem in the complex domain of electronic marketplaces and to inspire researchers from all over the world to develop distinctive software agents to a common exercise. In this thesis, a detailed study of intelligent software agents and a comprehensive investigation of the Trading Agent Competition will be presented. The design of the Risker Wise agent and a fuzzy logic system predicting the bid increase of the hotel auction in the TAC game will be discussed in detail
Tasks for Agent-Based Negotiation Teams:Analysis, Review, and Challenges
An agent-based negotiation team is a group of interdependent agents that join
together as a single negotiation party due to their shared interests in the
negotiation at hand. The reasons to employ an agent-based negotiation team may
vary: (i) more computation and parallelization capabilities, (ii) unite agents
with different expertise and skills whose joint work makes it possible to
tackle complex negotiation domains, (iii) the necessity to represent different
stakeholders or different preferences in the same party (e.g., organizations,
countries, and married couple). The topic of agent-based negotiation teams has
been recently introduced in multi-agent research. Therefore, it is necessary to
identify good practices, challenges, and related research that may help in
advancing the state-of-the-art in agent-based negotiation teams. For that
reason, in this article we review the tasks to be carried out by agent-based
negotiation teams. Each task is analyzed and related with current advances in
different research areas. The analysis aims to identify special challenges that
may arise due to the particularities of agent-based negotiation teams.Comment: Engineering Applications of Artificial Intelligence, 201
Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes
Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime” models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches
WARNING: Physics Envy May Be Hazardous To Your Wealth!
The quantitative aspirations of economists and financial analysts have for
many years been based on the belief that it should be possible to build models
of economic systems - and financial markets in particular - that are as
predictive as those in physics. While this perspective has led to a number of
important breakthroughs in economics, "physics envy" has also created a false
sense of mathematical precision in some cases. We speculate on the origins of
physics envy, and then describe an alternate perspective of economic behavior
based on a new taxonomy of uncertainty. We illustrate the relevance of this
taxonomy with two concrete examples: the classical harmonic oscillator with
some new twists that make physics look more like economics, and a quantitative
equity market-neutral strategy. We conclude by offering a new interpretation of
tail events, proposing an "uncertainty checklist" with which our taxonomy can
be implemented, and considering the role that quants played in the current
financial crisis.Comment: v3 adds 2 reference
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