Electronic Markets, Search Costs and Firm Boundaries

We study how firm boundaries are affected by the reduction in search costs when business-to-business electronic markets are adopted. Our paper analyzes a multi-tier industry in which upstream parts suppliers incur procurement search costs, and downstream manufacturers incur incentive contracting costs with these parts suppliers. We develop a model that integrates search theory into the hidden-action principal-agent model and characterize the optimal contract, showing that the delegation of search results in an outcome analogous to an effective increase in the search cost of the intermediary, reflected in the magnitude of the cutoff price in the second-best stopping rule. This contract is used to specify the manufacturer's make versus buy decision, and to analyze how the technological changes associated with electronic markets affect vertical organizational scope. Our main results show that when search is information-intensive, electronic markets will result in constant decreases in search costs that reduce the vertical scope of organizations. In contrast, when search is communication-intensive, electronic markets will result in proportionate reductions in search costs that lead to an increase in vertical integration; the latter outcome also occurs if search costs converge. We also discuss the implications of our results for the general problem of designing contracts that optimally delegate costly search to an intermediaryfirm boundaries, vertical integration, search, moral hazard, incentives, principal-agent, electronic markets, B2B markets.

Temperature and soil nutrient availability shape tree responses to elevation in the Australian Wet Tropics: growth, physiology, and chemistry

Arun Singh Ramesh investigated growth, physiology and biochemistry of Flindersia species distributed along an elevation gradient in the Australian Wet Tropics. He found that a mountaintop restricted species was sensitive to high temperatures and demonstrated limited acclimation of growth and physiological functioning compared to more widespread species

Phases and phase transitions of frustrated hard-core bosons on a triangular ladder

We study hardcore bosons on a triangular ladder at half filling in the presence of a frustrating hopping term and a competing nearest neighbor repulsion $V$ which promotes crystallization. Using the finite-size density-matrix renormalization group method, we obtain the phase diagram which contains three phases: a uniform superfluid (SF), an insulating charge density wave (CDW) crystal and a bond ordered insulator (BO). We find that the transitions from SF to CDW and SF to BO are continuous in nature, with critical exponents varying continously along the phase boundaries, while the transition from CDW to BO is found to be first order. The phase diagram is also shown to contain an exactly solvable Majumdar Ghosh point, and re-entrant SF to CDW phase transitions.Comment: 10 pages, 16 figure

Electronic Markets, Search Costs and Firm Boundaries

We study how electronic markets that facilitate broader inter-firm transactions affect the vertical scope of emerging IT-enabled extended enterprises. We do so by modeling firms in a three-tier value chain who are each connected to a common electronic market that facilitates direct business transactions across tiers, and that lowers the search costs associated with finding an appropriate trading partner for each of them. The extent to which search costs are reduced depends on the complexity of B2B search, and the nature of the supporting technologies that the electronic market facilitates. Variation in search costs affect firms across the value chain in three key ways: by a change in the transaction costs of interaction between firms; by a change in the contracting costs associated with outsourcing owing to changes in the costs of moral hazard for delegated search, and by a change in the price dispersion of upstream input commodities. We capture each of these effects in a new model that integrates search theory into the principal-agent framework, and establish that the optimal outsourcing contract has a simple "all or nothing" performance-based structure under fairly general assumptions. We then apply this model to contrast the effect that different information technologies have on the relative B2B search costs of different firms in the value chain, contrasting the predicted changes of proportionate, constant and convergent changes in search costs. When integrated with a detailed analysis of the nature of B2B search, these results predicts that when B2B search is information-intensive, electronic markets will facilitate an increase in outsourcing, market-based transactions and a reduction in the vertical scope of extended enterprises. In contrast, when B2B search is primarily communication-intensive, electronic markets will lead to tighter integration and an increase in the vertical scope of the extended enterprise. Our research suggest that the nature of the information technologies and of the business activities supported by an electronic market are crucial determinants of the organizational and industry changes they induce, and our results have important implications for a variety of industries in which both technological and agency issues will influence the eventual success of global IT-facilitated extended enterprise initiatives.Information Systems Working Papers Serie

Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment

Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. SCUC is run daily and requires state-of-the-art algorithms to speed up the process. The constraints and data associated with SCUC are both geographically and temporally correlated to ensure the reliability of the solution, which further increases the complexity. In this paper, an advanced machine learning (ML) model is used to study the patterns in power system historical data, which inherently considers both spatial and temporal (ST) correlations in constraints. The ST-correlated ML model is trained to understand spatial correlation by considering graph neural networks (GNN) whereas temporal sequences are studied using long short-term memory (LSTM) networks. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina (SC) 500-Bus system. Moreover, B-{\theta} and power transfer distribution factor (PTDF) based SCUC formulations were considered in this research. Simulation results demonstrate that the ST approach can effectively predict generator commitment schedule and classify critical and non-critical lines in the system which are utilized for model reduction of SCUC to obtain computational enhancement without loss in solution qualityComment: 8 Figures, 5 Tables, 1 Algorith

Machine Learning Assisted Approach for Security-Constrained Unit Commitment

Security-constrained unit commitment (SCUC) is solved for power system day-ahead generation scheduling, which is a large-scale mixed-integer linear programming problem and is very computationally intensive. Model reduction of SCUC may bring significant time savings. In this work, a novel approach is proposed to effectively utilize machine learning (ML) to reduce the problem size of SCUC. An ML model using logistic regression (LR) algorithm is proposed and trained with historical nodal demand profiles and the respective commitment schedules. The ML outputs are processed and analyzed to reduce variables and constraints in SCUC. The proposed approach is validated on several standard test systems including IEEE 24-bus system, IEEE 73-bus system, IEEE 118-bus system, synthetic South Carolina 500-bus system and Polish 2383-bus system. Simulation results demonstrate that the use of the prediction from the proposed LR model in SCUC model reduction can substantially reduce the computing time while maintaining solution quality.Comment: 6 Pages, 5 Figures, 3 tables, 1 algorith