Events

We consider the diffusion of two alternatives in social networks using a game-theoretic approach. Each individual plays a coordination game with its neighbors repeatedly and decides which to adopt. As products are used in conjunction with others and through repeated interactions, individuals are more interested in their long-term benefits and tend to show trust to others to maximize their long-term utility by choosing a suboptimal option with respect to instantaneous payoff. To capture such trust behavior, we deploy limited-trust equilibrium (LTE) in diffusion process. We analyze the convergence of emerging dynamics to equilibrium points using mean-field approximation and study the equilibrium state and the convergence rate of diffusion using absorption probability and expected absorption time of a reduced-size absorbing Markov chain. We also show that the diffusion model on LTE under the best-response strategy can be converted to the well-known linear threshold model. Simulation results show that when agents behave trustworthy, their long-term utility will increase significantly compared to the case when they are solely self-interested. Moreover, the Markov chain analysis provides a good estimate of convergence properties over random networks.

Vincent Leon (University of Illinois at Urbana-Champaign) – Limited-Trust in Diffusion of Competing Alternatives Over Social Networks & Apurv Shukla (Texas A&M University) – Differentially Private Online Resource Allocation

From viruses that cause zoonotic pandemics to the gut microbes that condition metabolism inside the body, microbes play crucial roles in human health and disease. Increasingly powerful scientific techniques are bringing to light the microbes inside and outside of our bodies and revealing how microorganisms connect individuals and even cross species.

But what to do about the microbes that we live with is a question that cannot be answered by microbiological tools alone. The human impact on the environment, including the consumption of antibiotics that alter the microbiome or the deforestation that increases the risk of emerging pathogens, demands historical and cultural analysis. Calls for closure of wet markets or culling of infected livestock in order to control pathogen spillover raise important questions about cultural differences in human-nonhuman relationships, the evaluation of risk, and the valuation of nonhuman life. Artists and anthropologists provide new tools to help us see the many possibilities of human life amidst the microbiome. After a century of intensive antibiotic use in healthcare and the livestock industry, can we imagine co-existence with microbes? How can we increase public understanding of the microbiome and its impacts on health and environment? What new alliances can be envisioned between microbial cultures and human cultures?

This workshop will initiate a conversation between biologists, anthropologists and artists to discuss venues of critical thinking on relations between microbes and health in a rapidly changing planet. The meeting will provide inspiration for an exhibition project and a collaborative white paper that aim for a reflexive account of microbial cultures.

Hank Tang & Denghui Yao (LightSpeed Studios Singapore) – How to Develop a Video Game in Real Life? – from the industry perspective

Nguyen Huu-Hai (Standard Chartered) – Functional Programming in Financial Markets

Hank Tang & Denghui Yao (LightSpeed Studios Singapore) – How to Develop a Video Game in Real Life? – from the industry perspective

Robin Foe & Tham Hon Hoe (OCBC Bank) – OCBC Cloud Native Journey

Robin Foe & Tham Hon Hoe (OCBC Bank) – OCBC Cloud Native Journey

HASS Colloquium Series: Supplying Colors and en-Route Science: German Synthetic Dyestuffs in Modern China in the Early 20th Century by Ms. Lejie Zeng

Decision trees are important both as interpretable models, amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient boosting. Their statistical properties, however, are not yet well understood. In particular, it is unclear why there is a prediction performance gap between them and powerful but uninterpretable machine learning methods. In this talk, we discuss how to bridge this gap partially via Hierarchical Shrinkage (HS), a post-hoc algorithm which regularizes the tree not by altering its structure, but by shrinking the prediction over each leaf toward the sample means over each of its ancestors. Furthermore, we discuss generalization lower bounds that reveal some of the inductive biases of tree-based methods, and how HS helps to overcome some of it.