The shift toward sustainable electricity systems is one of the grand challenges of the 21st century. Decentralized production from renewable sources, electric mobility, and related advances are at odds with traditional power systems where central, large-scale generation of electricity follows inelastic consumer demand. Information systems innovations can enable new forms of dynamic electricity trading that leverage real-time consumption information and that use price signals to incentivize sustainable consumption behaviors. However, the best designs for these innovations, and the societal implications of different design choices, are largely unclear. We are addressing these challenges through the Power Trading Agent Competition (Power TAC), a competitive gaming platform on which numerous research groups now jointly devise, benchmark, and improve IS-based solutions to the sustainable electricity challenge. Based on the Power TAC community's results, we give preliminary empirical evidence for the efficacy of competitive gaming platforms, and for the community's contributions toward resolving the sustainable electricity challenge.
The proliferation of online auctions has attracted significant research interest in understanding real-life bidding behavior. However, most of the empirical work has focused on business-to-consumer (B2C) auctions. A natural question is whether the findings obtained from B2C auctions are applicable to business-to-business (B2B) auctions, which often involve much higher stakes. In this paper, we examine how professional bidders choose their bidding strategies in multichannel, sequential B2B auctions. Using an extensive data set from the world's largest B2B market for cut flowers, we find a stable taxonomy of bidding behavior and identify five distinctive bidding strategies. In addition, we demonstrate that bidders' choice of strategies is associated with their demand, budget constraint, and transaction cost. These findings challenge the conventional view that bidders' bidding strategies will converge as they gain experience. We also analyze the economic impacts of different strategies. Our results provide useful implications for practical design of B2B auctions.
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.
Electronic markets have been a core topic of information systems (IS) research for last three decades. We focus on a more recent phenomenon: smart markets. This phenomenon is starting to draw considerable interdisciplinary attention from the researchers in computer science, operations research, and economics communities. The objective of this commentary is to identify and outline fruitful research areas where IS researchers can provide valuable contributions. The idea of smart markets revolves around using theoretically supported computational tools to both understand the characteristics of complex trading environments and multiechelon markets and help human decision makers make real-time decisions in these complex environments. We outline the research opportunities for complex trading environments primarily from the perspective of design of computational tools to analyze individual market organization and provide decision support in these complex environments. In addition, we present broad research opportunities that computational platforms can provide, including implications for policy and regulatory research.