Potential based prediction markets: a machine learning perspective

A prediction market is a special type of market which offers trades for securities associated with future states that are observable at a certain time in the future. Recently, prediction markets have shown the promise of being an abstract framework for designing distributed, scalable and self-incentivized machine learning systems which could then apply to large scale problems. However, existing designs of prediction markets are far from achieving such machine learning goal, due to (1) the limited belief modelling power and also (2) an inadequate understanding of the market dynamics. This work is thus motivated by improving and extending current prediction market design in both aspects. This research is focused on potential based prediction markets, that is, prediction markets that are administered by potential (or cost function) based market makers (PMM). To improve the market’s modelling power, we first propose the partially-observable potential based market maker (PoPMM), which generalizes the standard PMM such that it allows securities to be defined and evaluated on future states that are only partially-observable, while also maintaining the key properties of the standard PMM. Next, we complete and extend the theory of generalized exponential families (GEFs), and use GEFs to free the belief models encoded in the PMM/PoPMM from always being in exponential families. To have a better understanding of the market dynamics and its link to model learning, we discuss the market equilibrium and convergence in two main settings: convergence driven by traders, and convergence driven by the market maker. In the former case, we show that a market-wise objective will emerge from the traders’ personal objectives and will be optimized through traders’ selfish behaviours in trading. We then draw intimate links between the convergence result to popular algorithms in convex optimization and machine learning. In the latter case, we augment the PMM with an extra belief model and a bid-ask spread, and model the market dynamics as an optimal control problem. This convergence result requires no specific models on traders, and is suitable for understanding the markets involving less controllable traders

Multi-period Trading Prediction Markets with Connections to Machine Learning

We present a new model for prediction markets, in which we use risk measures to model agents and introduce a market maker to describe the trading process. This specific choice on modelling tools brings us mathematical convenience. The analysis shows that the whole market effectively approaches a global objective, despite that the market is designed such that each agent only cares about its own goal. Additionally, the market dynamics provides a sensible algorithm for optimising the global objective. An intimate connection between machine learning and our markets is thus established, such that we could 1) analyse a market by applying machine learning methods to the global objective, and 2) solve machine learning problems by setting up and running certain markets

M2-like macrophages in the fibrotic liver protect mice against lethal insults through conferring apoptosis resistance to hepatocytes.

Acute injury in the setting of liver fibrosis is an interesting and still unsettled issue. Most recently, several prominent studies have indicated the favourable effects of liver fibrosis against acute insults. Nevertheless, the underlying mechanisms governing this hepatoprotection remain obscure. In the present study, we hypothesized that macrophages and their M1/M2 activation critically involve in the hepatoprotection conferred by liver fibrosis. Our findings demonstrated that liver fibrosis manifested a beneficial role for host survival and apoptosis resistance. Hepatoprotection in the fibrotic liver was tightly related to innate immune tolerance. Macrophages undertook crucial but divergent roles in homeostasis and fibrosis: depleting macrophages in control mice protected from acute insult; conversely, depleting macrophages in fibrotic liver weakened the hepatoprotection and gave rise to exacerbated liver injury upon insult. The contradictory effects of macrophages can be ascribed, to a great extent, to the heterogeneity in macrophage activation. Macrophages in fibrotic mice exhibited M2-preponderant activation, which was not the case in acutely injured liver. Adoptive transfer of M2-like macrophages conferred control mice conspicuous protection against insult. In vitro, M2-polarized macrophages protected hepatocytes against apoptosis. Together, M2-like macrophages in fibrotic liver exert the protective effects against lethal insults through conferring apoptosis resistance to hepatocytes

Institutional determinants of construction safety management strategies of contractors in Hong Kong

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