Sriram Dasu

Negotiations associated with technology adoption can take months or years and agreements are often made close to deadlines. Existing theories attribute agreement delay and the deadline effect in bilateral negotiations to either asymmetric information or behavioral constraints.
However, high-technology industries are often characterized by deep cooperation, effective
communications, rational decisions, and uncertain demand for new technologies. To study
the driving forces and consequences of delayed agreements in high-technology industries, we
build a bilateral, dynamic bargaining model featuring uncertain demand facing the seller, information
symmetry, and a deadline. We discover that incentives to learn about the seller’s demand drive delay of agreements. With better information, the seller can possibly sell the manufacturing capacity to buyers who would like to pay more; however, the buyer can also benefit from learning because the seller must make concessions if they find the demand to be
weak. Thus, contrary to most existing theories, delay can benefit both negotiators, and even
with the deadline effect present, their expected payoffs can be improved by extending the
deadline. In addition, we find that the delay tendency increases as more technology adopters
appear and the belief of high demand becomes stronger.

Balancing demand and supply of electricity is one of the most critical tasks that utility firms perform to maintain grid stability and reduce system costs. Demand-response programs are among strategies that utilities use to reduce electricity consumption during peak hours and flatten the energy consumption curve.
Direct load control contracts (DLCCs) are a class of incentive-based demand-response programs that allow utilities to assign “calls” to customer groups to reduce their energy usage for a pre-specified amount and for a given length of time.

Given the rapid expansion of such contracts, in this paper, we develop an integer stochastic dynamic optimization problem for executing DLCCs that minimize total system cost subject to monthly and annual constraints on the number of times and hours customers can be called. We develop a hierarchical approximation approach, which consists of an annual problem and monthly problems, to solve the DLCC implementation problem effectively and in a reasonable amount of time. Motivated by the practice in a large utility firm in California, we incorporate a reduce-to-threshold policy that attempts to flatten energy consumption curves whenever demand exceeds a given threshold. We verified the quality of our proposed approach on real data from the California Independent System Operator (CAISO), which is the umbrella organization of the utility firms in California, and measured the quality of our solution against a lower bound.

A large utility firm in California implemented our model and informed us that the additional reduction in cost was approximately 4%. Our sensitivity analysis reports the impact of managerial concerns on some policies to

(1) Problem definition: We study the sustainability of group lending under joint liability contracts when borrowers are subject to strategic default. The benevolent lender optimizes the borrower's welfare while covering her costs of lending, and social ties between borrowers serve as monitoring tools.

(2) Academic/Practical relevance: To the best of our knowledge, our work is the first the- theoretical study of this problem that models borrowers' social network structures as patterns of network monitoring.
(3) Methodology: The methodology employed in this paper is a reverse game theory or mechanism design.
(4) Results: The results show that a higher average degree of centrality and connectivity in the borrower network allows for more favorable contract terms (i.e., a higher loan ceiling and a lower repayment amount). Accordingly, a borrower network with a complete structure qualifies for the best contract terms. Intuitively, a complete network, which provides full monitoring, in- duces the highest level of cooperation among borrowers and guarantees the highest level of group performance (i.e. a higher chance of game continuation and a higher repayment rate). Further, we argue that while for smaller groups, the highly decentralized ring networks (egalitarian) may be better.