https://www.adventuresinwhy.com/tag/decision-making/Recent content in Decision Making on Adventures in WhyHugoen-usbob@ConvexAnalytics.com (Bob Wilson)bob@ConvexAnalytics.com (Bob Wilson)© Bob Wilson {year}Wed, 22 Apr 2026 00:00:00 +0000https://www.adventuresinwhy.com/publication/admm-bilinear-minimax/Wed, 22 Apr 2026 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/publication/admm-bilinear-minimax/https://www.adventuresinwhy.com/post/robust_portfolio_optimization_diminishing_returns/Tue, 28 Dec 2021 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/robust_portfolio_optimization_diminishing_returns/In our last post, we discussed how model uncertainty poses a risk when allocating resources among productive assets. In this post, we expand the discussion to models with diminishing returns. Such models are common in economics. As before, we can incorporate model uncertainty directly into the problem, achieving good performance regardless of the true model, with minimal impact to nominal performance. Robust optimization is both powerful and practical.https://www.adventuresinwhy.com/post/robust_portfolio_optimization_glm/Fri, 24 Dec 2021 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/robust_portfolio_optimization_glm/Often we run an A/B test in order to inform some decision. But every A/B test involves uncertainty, no matter the sample size. This uncertainty poses a risk to our decision, which can be hedged by a process analogous to diversifying an investment portfolio. Finding a robust-optimal portfolio is both practical and fast.https://www.adventuresinwhy.com/post/alternative_to_causal_inference/Sun, 10 Oct 2021 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/alternative_to_causal_inference/Some of the most important questions data scientists investigate are causal questions. They’re also some of the hardest to answer! A well-designed A/B test often provides the cleanest answer, but when a test is infeasible, there are plenty of other causal inference techniques that may be useful. While not perfect, these techniques are much better than the alternative: ad hoc methods with no logical foundation.https://www.adventuresinwhy.com/post/unshackle-yourself-statistical-significance/Mon, 12 Oct 2020 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/unshackle-yourself-statistical-significance/Don’t be a prisoner to statistical significance. A/B testing should serve the business, not the other way around!https://www.adventuresinwhy.com/post/descriptive/Sat, 13 Apr 2019 00:00:00 +0000bob@ConvexAnalytics.com (Bob Wilson)https://www.adventuresinwhy.com/post/descriptive/The great successes of Machine Learning in recent years are based on our ability to extrapolate and predict based on data. The next big step is learning and leveraging the relationship between cause and effect to prescribe what action to take.