Decision Support Systems For Strategic Dispute Resolution

Abstract

Disputes and lawsuits are quite common in business and are often a source of significant liabilities. We conjecture that measurement challenges and lack of adequate analysis tools have greatly inhibited the ability of the General Counsel’s offices in selecting the best mode for the resolution (i.e. litigation vs. out-of-court settlement) of business conflicts and disputes. Easily quantified direct costs (e.g., out-of-pocket expenses related to pursuing and defending against litigation) tend to be considered, whereas the more difficult-to-quantify indirect risks and costs (e.g., damaged relationships with customers and potential alliance partners, including reputational harm) which may be quite significant, tend to be ignored. We also hypothesize that the benefits of Alternative Dispute Resolution (ADR) strategies may have been muted because of the failure to assess the real magnitude of not-easily-quantified indirect risks and costs. We propose two Decision Support Systems (DSSs), one for a macro-level analysis and one for a micro-level (i.e. case by case analysis), to alleviate the measurement and analysis problem.

In the proposed DSSs, the underlying decision engine makes use of operations research tools such as decision trees, logic modeling, Monte-Carlo Markov-Chain (MCMC) and fuzzy logic simulations. By providing the means to gather decision-relevant information, especially on difficult-to-measure soft costs, we have attempted to reduce the “decision making risk” for the General Counsel’s offices. In the process, we have also furnished some ways to reach more informed assessments to support litigation risk management strategies and decisions.