Anagha Satish

The most obvious strategy for reducing polarization in social networks is to connect people with opposing views. However, this is neither realistic nor likely to result in productive connections or conversations. To reduce polarization in networks, we propose a two-dimensional model with two opinion sets, each representing a different issue. We introduce a new \textitcommon ground strategy to more realistically reduce polarization on a primary issue, by creating connections between individuals with similarities on a separate, secondary issue. We propose a new polarization metric and measure our common ground strategy’s impact on polarization on both real-world networks. We find a significant reduction in polarization through utilization of this strategy.

We propose a novel algorithm for integrating gradient information into Consensus Based Optimization (CBO), a recently proposed multi-particle gradient-free optimization method. During each iteration, a subset of particles are updated using local gradient information, while others are updated using a traditional CBO step. We propose a method for subset selection and investigate its empirical performance. The algorithm combines gradient and gradient-free optimization to encourage exploring the state space while maintaining fast convergence. We investigate the tradeoff between accuracy and computational cost when adjusting the number of gradient evaluations. When applied to classification tasks in machine learning, the proposed algorithm attains a similar accuracy to ensemble gradient methods based on Gradient Descent or Adam at a reduced computational cost.