http://www.hpl.hp.com/personal/Thomas_Sandholm/sandholm2007a.pdf Applying Occam's razor to statistics to enable risk preference multiplexing. We study stochastic models to mitigate the risk of poor Quality-of-Service (QoS) in computational markets. Consumers who purchase services expect both price and performance guarantees. They need to predict future demand to budget for sustained performance despite price fluctuations. Conversely, providers need to estimate demand to price future usage. The skewed and bursty nature of demand in large-scale computer networks challenges the common statistical assumptions of symmetry, independence, and stationarity. This discrepancy leads to underestimation of investment risk. We confirm this non-normal distribution behavior in our study of demand in computational markets. Thomas Sandholm and Kevin Lai 2007-07-12 14:08:00 tycoon risk qos http://www.hpl.hp.com/research/idl/papers/insurance/index.html How to transform risk averse managers into risk neutral ones risk We present a new mechanism for encouraging risk taking within organizations that relies on the provision of decision insurance to managers. Since insurance increases the likelihood of free riding, we also introduce a technique that mitigates this moral hazard by automatically identifying the social network around the manager and using it as a monitoring group. We show that three possible regimes exist. In the first one, managers contribute to production but avoid risky projects. In the second, managers take on risky projects without free riding. In the third, they free ride. We establish the conditions for the appearance of each of these regimes and show how to adjust the mech nism parameters so as to get the highest expected payoff for the firm in spite of its risk-adverse managers. Tad Hogg and Bernardo A. Huberman 2007-01-26 01:41:00