There are lots of low-probability, high-consequence events that we are not going to be able to model, no matter what we do with the parameter values. a single warehouse. Their experimental findings lead them to suggest a mathematical form for the S-shaped value function, V(x). One of the advantages of computerization has been that it has become easier to synthesize data from a statistical model (in fact, the first use of a computer I experienced was my math teacher bringing his ZX81 into statistics class for that purpose). If there’s an unusually warm spring in the Sierra Nevada mountains, all the snow melts into water and has to be released through the dams, so there’s a lot of hydropower and electricity gets cheap for a while in Northern California…but then that hydro isn’t available later in the year, so if there’s a warm summer and the natural gas “peaker” plants are in heavy use, the electricity price will be highly dependent on the cost of natural gas, which could be high because…well, you get the idea. However, evidence suggests individuals often exhibit a significant status quo bias: the more options available, the stronger the bias for the status quo; the stronger an individual’s preference for a selected alternative, the weaker said bias (Samuelson and Zeckhauser 1988). Georges Dionne, Scott E. Harrington, in Handbook of the Economics of Risk and Uncertainty, 2014, Although the theory of decision making under uncertainty has frequently been criticized since its formal introduction by von Neumann and Morgenstern (1947), it remains the workforce in the study of optimal insurance decisions. And they wouldn’t even have to do this with their own trading operations: there are plenty of companies that will manage your energy price risk–for a fee. As a consumer of energy, why not just focus on predicting your consumption, then decide what’s the max price spike that our P&L is willing to bear and finally buy an energy option at that price and be done with it? This seems like a reasonably cost effective way to generate two points of comparison. Forecasting energy prices is a fools game. Even to say that the policy provides “the specified level of protection” you have to be very careful if that is the expected level of protection or some actual property of the sample paths. Structural uncertainty •Modelling or structural uncertainty –Alternative model structures or assumptions could generate different results •Model validity –Assess how accurately available info characterised –Typically no source for external validation •Value judgements •Can identify some models as … What makes this non-standard, at least as far as I know, is the multi-month, multi-facility optimization. The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions. Decision Making Under Uncertainty: Models and Choices [Holloway, Charles A.] It is that the bad ones are so much more expensive than the good ones are profitable. For a lot of businesses the current pandemic has put them in exactly that situation, in fact. I’d like to return to the general issue that your post raises. Consider the expected-utility representation, where p and q are simple probability distributions on X=X1× … ×Xn and u on X is unique up to a positive affine transformation au+b, a>0. We can check the market price per MWh for buying electricity in that month. In insurance problems, it is well known that the worst case are when there is aggregate risk: when the bad things that can happen, happen to many agents at the same time. If the company as a whole wants to avoid spending far more than expected for energy, they are already partially covered simply by being spatially diverse. I think my last statement is debatable, and perhaps wrong. This is sort of model complexity creep that leads to disaster I feel. Since 1997 he has taught courses in applied probability, stochastic systems, queuing models, decision-making, operations research, and statistics while being on the faculty at Pennsylvania State University and Texas A&M University. It’s these hourly numbers that we use for the actual calculation. 2010). The classical expected utility model remains, however, the most useful model for insurance analysis. So we add one more layer of sampling to the method I described above. Actually I think the model that we are contemplating should be useful for a long time, but due to continuing changes in the markets and the company’s operations the input parameters are never going to be estimated with decent precision. Rahul, uncertainty: irreversibility, discounting, and the consequences of the standard expected utility approach to representing uncertainty. I’m guessing not, if the company has the option to DD. This tendency toward overestimation may lead consumers to be less cautious in their financial decision-making (Perry 2008). Explore the latest questions and answers in Decision Making Under Uncertainty, and find Decision Making Under Uncertainty experts. Seems like the question you’re being asked is: is there some way to make a major leap in modelling beyond the standard hedge model already available on the market? When I say ‘extremely high’ I mean it: the price per MWh was about 200 times the typical price for a few hours, and about 30 times normal for several days. I think that if they’re hedged adequately against a 95th percentile fiscal quarter, whatever they mean by that exactly, and they experience a 99th percentile fiscal quarter, that will hurt but won’t be crushing. We have a simple model based on historical data, and we can quantify how well it works. Forecasting energy prices is not a fool’s game, it’s a necessary feature of the energy markets. Well actually, the status quo may not be a “no hedge” but how much better is your model over whatever hedge the company may do heuristically anyways. 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