Pushing the Limits of Rational Agents: The Trading Agent Competition for Supply Chain Management

Collins, John; Ketter, Wolfgang; Sadeh, Norman

doi:10.1609/aimag.v31i2.2287

Cited by 35 publications

(20 citation statements)

References 34 publications

(75 reference statements)

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“…Competitive Benchmarking, as we define it here, is rooted in the competitive research approach pioneered by the Trading Agents community (Greenwald and Stone, 2001;Collins et al, 2010a;Wellman, 2011;Ketter and Symeonidis, 2012), which aims to deploy techniques from Artificial Intelligence and other computational disciplines to trading applications. Trading Agent Competitions (TAC) challenge researchers to devise software agents for complex, uncertain environments such as supply chains (Arunachalam and Sadeh, 2005; and advertisement auctions (Jordan and Wellman, 2010), to benchmark them in direct competitions with each other, and to improve them iteratively.…”

Section: Related Workmentioning

confidence: 99%

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Ketter

Peters

Collins

et al. 2016

MISQ

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Abstract. Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) "individuals, organizations, and markets" and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation.Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions.

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Section: Related Workmentioning

confidence: 99%

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Ketter

Peters

Collins

et al. 2016

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“…특히 사회적 및 경제적으로 중요하고 연구자들로 하여금 흥미를 유발하기에 충분한 도메인을 배경으로 한다 (Bichler et al, 2010). 일찍이 TAC은 2000년도를 시작으로 TAC Classic (Wellman et al, 2003), TAC SCM(Trading Agent Competition for Supply Chain Management) (Collins et al, 2010), CAT(Market Design Competition) (Cai et al, 2009), TAC AA(Trading Agent Competition for Ad Auctions) (Jordan and Wellman, 2009) 등을 비롯하여 최근 Power TAC까지 세계 적으로 활발히 개최되고 있다. 이상 TAC의 역사는 <Figure 1> 에 요약되어 있다.…”

Section: 국내 전력 거래 조사unclassified

Estimating Optimized Bidding Price in Virtual Electricity Wholesale Market

Shin¹,

Lee²,

Cha³

et al. 2013

Journal of Korean Institute of Industrial Engineers

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Power TAC (Power Trading Agent Competition) is an agent-based simulation for competitions between electricity brokering agents on the smart grid. To win the competition, agents obtain electricity from the electricity wholesale market among the power plants. In this operation, a key to success is balancing the demand of the customer and the supply from the plants because any imbalance results in a significant penalty to the brokering agent. Given the bidding on the wholesale market requires the price and the quantity on the electricity, this paper proposes four different price estimation strategies: exponentially moving average, linear regression, fuzzy logic, and support vector regression. Our evaluations with the competition simulation show which strategy is better than which, and which strategy wins in the free-for-all situations. This result is a crucial component in designing an electricity brokering agent in both Power TAC and the real world. †

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“…The annual Trading Agent Competition for Supply Chain Management (TAC SCM) (Collins et al, , 2010b) is a competitive agent-based simulation of an abstract supply chain environment, where software agents make all the decisions. TAC SCM simulates a market where six autonomous agents compete to maximize profits over a one-year life cycle for a set of computer models.…”

Section: A Case Study: the Trading Agent Competition For Supplychain mentioning

confidence: 99%

“…Our approach is tested by embedding our computational methods in a software agent that operates in the Trading Agent Competition for Supply Chain Management (TAC SCM) (Collins et al, 2010b). Experimental results show that our approach performs better than traditional predictive modeling methods.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes

Ketter

Collins

Gini

et al. 2012

Information Systems Research

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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 addresses 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, a supply chain environment characterized by competitive procurement, sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and long-term resource allocation decisions. Results show that our method outperforms more traditional short- and long-term predictive modeling approaches.

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Pushing the Limits of Rational Agents: The Trading Agent Competition for Supply Chain Management

Cited by 35 publications

References 34 publications

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Estimating Optimized Bidding Price in Virtual Electricity Wholesale Market

Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes

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