Solution of optimization problems for group decision making

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Taking decisions that involve and affect multiple people is ubiquitous in personal, governmental, and enterprise settings. Such decisions always involve trade-offs and are bound to make some people happier than others. In addition, polarization has been increasingly present in our society and decisions that achieve a better compromise and avoid such polarization have become increasingly important. The classic approach for group decision commonly relies on binary for/against voting which overlooks the complexity of decision making and inherently divides people into winners and losers. Furthermore, this superficial engagement of participants fails to capture insights from the stakeholders and creates an imbalance that favors individuals with political and social power. The alternative is to harness crowd wisdom by enabling an environment to collect rich community feedback that extends beyond binary voting and allows the stakeholders to evaluate various options, propose ideas, and weigh priorities. The loop is then closed by showing the trade-offs among the different options and explaining the decisions in order to eliminate polarization and promote broadly supported decision.Ethelo is a Canadian company that provides an internet technology platform with a mission to promote democratic policy-making and help organizations identify smart and fair decisions. The purpose of this proposal is to study one of the key components in the Ethelo engine, namely an optimization problem that attempts to characterize and obtain the best decisions according to a criterion that includes such fairness components. The expected outcome is to make the Ethelo tool significantly faster, greatly improving on the usability and applicability of their tool.