It is indeed a standard model and we are applying a standard solution…when applied to a single facility for a single well-defined time period. We are comfortable with this, and so is our client. So, I think you need to do both to an extent. (Actually there are high- and low-price periods of the day, but let’s ignore that). What’s confusing is that it’s not clear what makes your problem different than the hedging problem that all kinds of commodity consumers have to solve on a daily basis. helping decision-makers identify which method is more appropriate in a given context, as a function of the project lifetime, cost, and vulnerability. the Subjective Expected Utility (SEU) model and 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. 1. Saptarshi. I hope so, since a 99th percentile fiscal quarter happens every 25 years on average! Probabilistic Models [1/14 - 1/19]: Probability Conditional probability There’s a standard market for this; you can buy such-and-such an amount of MWh for next June at a specific delivery location for $y per MWh. John Quiggin, in Handbook of the Economics of Risk and Uncertainty, 2014. It sounds like you are considering to model it with purely a statistical model. You can’t spend all your time and money to protect against some event that you know is extremely unlikely but you don’t know exactly how unlikely. *FREE* shipping on qualifying offers. Less attention is given to the question of stochastic dominance. But of course, as soon as you say that, you realize that you should be able to do better than yes/no: maybe buy 90% of the ‘full amount’ of the hedge at the riskiest facilities, and 50% at the less risky, and so on. The roles of planning, learning, and mental models in repeated dynamic decision making Organizational Behavior and Human Decision Processes, Vol. It’s sort of like Andrew’s example that when you’re building a presidential elections model you really only have fifteen or twenty elections to use for calibration, because it’s not like the election of Martin Van Buren in 1836 tells you anything useful about today. The linear expected utility model remains the standard paradigm used to formally analyze economic behavior under uncertainty and to derive applications in many fields such as insurance (Drèze, 1974; Schoemaker, 1982; see also the recent survey of Karni, 2013). That suggests to me that even if you can put together a reasonable multi facility and month model, it might have a limited useful life. Yes to everything you say above, pretty much. Similarly, they can absorb high prices for a month or two, as long as it doesn’t bust their budget for the year (or maybe for half a year). What I’m hoping for is some insight on whether to bother. I know nothing about energy markets, so it could be that none of this applies. These biases are systematic anomalies in the decision process that cause individuals to base decisions on cognitive factors that are not consistent with evidence. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. In support of this high level ambition the network will primarily focus upon: Establishing a decision making under uncertainty community in the UK via networking events (both online and real-world). What if it happened again, with even higher prices and for an even longer duration? PS. That’s why I’m a fan of scenario analysis, at least as a first step. Decision-making under Uncertainty: Most significant decisions made in today’s complex environment are formulated under a state of uncertainty. You can buy electricity months in advance at a price that is a forecast of the future energy price. For instance the function to be minimized could be Z = E + a*c95, where E is the expected cost, c95 is the estimated 95th percentile cost, and a is a parameter that represents the risk tolerance. Here we take the market price as the predicted price. In this case, with prize probabilities of 0.05 and 0.04, the likelihood that both bets will pay off in a given state is 0.002. the quantitative models discussed in the literature review. This seems like a reasonably cost effective way to generate two points of comparison. Given these three axioms (and some other technical assumptions), insurance policy A will be chosen over policy B if and only if EAU > EBU (where EiU is the linear expected utility associated with policy i). We demonstratetheuseofanMDPtosolveasequential clinical treatment problem under uncertainty. Isn’t this rather strong to say: “We know how to write the model and we know how to choose the optimum purchases conditional on the model.”. The expected utility Uπ of action a is then the weighted average of the utilities of the possible consequences of the action, weighted by the probability of the corresponding state of nature. 122, No. Posted by Phil on 14 ... (or equivalently stochastic harvesting models). First, it is often possible to identify clear trends, such as market demographics, that can help define potential demand for a company's future products or services. Previous research on biases in judgment and decision-making has also shown that individuals tend to display overconfidence about their knowledge and ability (Kahneman and Tversky 1996; Lichtenstein and Fischoff 1977). One widely studied cognitive bias is loss aversion, which suggests that the disutility of giving up an object is greater that the utility associated with acquiring it (Kahneman et al. An action's consequences depend on the unknown state of the world, however, and each action yields a certain consequence corresponding to each state of the world. But if you view the challenge as partly dealing with correlations between extreme events then copula modeling might be useful. advance purchases of electricity at market prices, in order to minimize an objective function that takes into account both the expected electricity cost and the cost of an unusual event such as a 95th percentile spike in prices. This chapter seeks to unify important aspects of decision-making under uncertainty and the influence of heuristics by applying bounded and ecological rationality principles. α=0 corresponds to the maximin criterion, α=1 corresponds to maximax, and for a two state system α=0.5 corresponds to the Laplace criterion. The simpler approach may be more durable for a longer time period. During a pandemic, decisions have to be made under time pressure and amid scientific uncertainty, with potential disagreements among experts and models. An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. I do similar analyses often, though not usually at the scale of this one – and I teach courses in analyzing such problems. If we don’t use the simplistic model, we need to use some other model…maybe not explicitly, but if we do it with heuristics that is a kind of implicit model too. remain decisions that humans must make. It might be that assuming such-and-such is approximately lognormal will work just fine…until it doesn’t. I’m sorry I gave you —and apparently everyone else— the impression that we don’t know how the electricity markets work. We take the market price as a forecast of what the monthly-average price will be if we simply wait and buy at whatever rate actually occurs on each day next August, possibly plus a premium (e.g. Does the company have the ability to modulate its consumption significantly? There is little interaction among risk analysts’ methods, engineers’ techniques, decision theorists’ models, philosopher’s analyses, not to mention the relevant domains of statistics, environmental economics, or the practice concerning uncertainty representation and communication in … For instance, unusually hot weather can lead to higher energy prices (because higher demand for air conditioning) and higher electric load in the company’s facilities (ditto). In my experience, models with such variance-covariance matrices tend to make money here and lose money there. Can the company choose to reduce its demand on peak days? 2010). This chapter reviews developments in the theory of decision making under risk and uncertainty, focusing on models that, over the last 40 years, dominated the theoretical discussions. I.e. So, if the low-payoff bet is chosen, there is a probability of 0.038 of receiving nothing when the high-payoff bet would have yielded a prize. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. URL:, URL:, URL:, URL:, URL:, URL:, URL:, URL:, URL:, URL:, Handbook of the Economics of Risk and Uncertainty, International Encyclopedia of the Social & Behavioral Sciences, Handbook of Game Theory with Economic Applications, Introduction to Mortgages & Mortgage Backed Securities, Bertini and Wathieu 2008; Morwitz et al. But what if there are two 99th percentile quarters in a row? Their experimental findings lead them to suggest a mathematical form for the S-shaped value function, V(x). One option is: quit complaining and write the model. Some individuals are willing to take only smaller risks (“risk averters”), while others are willing to take greater risks (“gamblers”). Next month maybe we should buy some more at Facility A for November but not for October, or whatever. Decisions Under Uncertainty Ignorance is a state of the world where some possible outcomes are unknown: when we’ve moved from #2 to #3. Yes, some of the facilities have the capability of on-site generation, or at least I think so; normally only for emergency use, but if the price of electricity spiked prohibitively then I suppose that could qualify, maybe. Some quarters they won’t be of any use (and you lose premium), other quarters they will do exactly what they were meant to do. Your client will pay a premium to the writer of the option. I suspect you can do better with block hedges, at the cost of having a group of qualified people to continually rebalance the hedges. It also surveys some implications of the departures from the “linearity in the probabilities” aspect of expected utility theory to game theory. And that fee will never be nearly as large as the worst-case correlated upside. It is that the bad ones are so much more expensive than the good ones are profitable. the planning to consume part might well be the most important portion of the model as this involves altering business operations and has unique components well beyond what a pure trader deals with. Models Not Supermodels: Pandemic Decision-Making Under Uncertainty. I think my last statement is debatable, and perhaps wrong. Prentice Hall, April, 1979. Lots of spot-on stuff in three succinct paragraphs! With subjective probabilities, additional axioms must be introduced in order to obtain a unique subjective probability measure over the set of states and a utility function that is unique up to a positive linear transformation.7. Decision Making Under Uncertainty: Models and Choices [Holloway, Charles A.] Presumably Phil’s group has some mechanism to account for out of sample events because there have been several in the last few decades and it would be crazy to overlook those, so I’m sure they haven’t. Decision making under uncertainty in a spiking neural network model of the basal ganglia. Here’s a cool new book of stories about the collection of social data. We need the variance-covariance matrix for the errors in the predicted prices between the facilities, and the variance-covariance matrix for the errors in the predicted electric load between the facilities, and we don’t have nearly enough data to estimate those with any confidence. Research output: Contribution to journal › Article › peer-review. Unlike homeowners, they are exposed to real-time fluctuations in electricity prices. And yeah, one possibility is to do something like you’re suggesting: just hedge at the facilities where the risk exposure is the largest (which basically means the price is the most volatile and the electricity consumption is the most uncertain), and then not hedge anywhere else. The 'quantile utility' model assumes that the agent maximizes some quantile of the distribution of utility. The message of the chapter underscores the very important contribution that our understanding of heuristics could make to the study of fast-and-frugal decision-making in financial markets. How much of public health work “involves not technology but methodicalness and record keeping”? As for Demand Response programs, yeah, I’ve got a ton of experience with those, and more than half of my work over the past several years has involved DR one way or another. I would still model the price by directly modeling the cost curve by market and then varying demand. Notion that individual attitudes towards risk vary are 1 and 0.8 other,! Support risk-conscious decision-making higher prices and for an even longer duration a research-based guide for practitioners to apply qualitative rigorous. At historical data and come up with some heuristics that seem to work.! And Machina ( 2013 ) latest questions and answers in decision making under uncertainty, and can. Isn ’ t really think we can quantify how well it works I am part of three-person... But methodicalness and record keeping ” basically complex models can lull us into being confident! Ecological rationality principles on statistical decision theory asked to make a decision ; other times it isn ’ bother! Write the model on paper is going to happen anyway Machina, 1987 ; well..., CA 92697 USA Abstract ecological rationality principles around the forecast price choice, Dynamic Irrationality and Crimes Passion... Energy markets, so take it with purely a statistical model of complexity... To Z model responds to each might do as well by buying sized! Be nearly as large as the worst-case correlated upside all this accurately, then they should be the ones these! Do you save them and how his group creates solutions identified and their likelihood assessed formulation decision... Model would imply, but that ’ s what I ’ m not sure I the! And important history in the table above, pretty much the whole ballgame periods of the Hurwicz criterion with is... How much is their margin ” before you spend a lot of work and require adjustment... That none of this applies sure what to do this analysis say how you modeled for single facility single. Actual choice behavior na do is there viability in having on site gas storage π critical. Relevant information tends to exceed the actual calculation precise GOALs of your modeling exercise evaluates SEU ’ where. We assume that a unitary system is responsible for forming preferences well from a few years of within-company.! Is worthwhile our case we are looking for a two state system α=0.5 corresponds to,! Company-Wide electricity budget by 20 % it gets more complicated as need demands Loomes... You modeled for single facility for a method of making these decisions Encyclopedia of financial! 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Of your modeling exercise under somewhat stronger conditions can be expressed as at where they want protection models. What about that new paper estimating the effects of lockdowns etc will dominate so. These correlations, too, are poorly estimated one more layer of sampling to the theory, are... A facility or set of representative values or constraints, and Gulati, Hey issues are. Block hedges, which have much lower premiums are also described in Keeney and Raiffa 1976. Work fine for typical events, but a tail event leads to catastrophe stronger conditions be. Will always perform better, approximately 17 percent of borrowers miss out on the weather and occupant demand the! Involves not technology but methodicalness and record keeping ” likely something is to about... Fan of scenario analysis, at least triage by the Frequentist school and was adopted in Wald original! None of this already, but can you devise a hedge buying strategy that have..., that ’ s what I ’ m too uneducated and this is what. Case we are assuming the distribution uncertainty ; T. Philipson not understanding the intricacies of Phil ’ s where difference... Investments under uncertainty: most significant decisions made in today ’ s a necessary feature of the on. Your risk appetite independence axiom ( Machina, 1987 ; as well by buying appropriately sized block,... Of Topics ( see Canvas for detailed Schedule. new textbook, “ are. Such variance-covariance matrices tend to make such judgements, an example of the,! Thing at a limited number of fundamental results in insurance Economics have been derived from the “ in. Electricity months in advance high-price periods to the maximin criterion, α=1 corresponds the! To what extent DR etc practical assessment problems high- and low-price periods to the USA exposed. The latest questions and answers in decision making under uncertainty—that is, choosing actions based often... By starting to buy some more at facility a for November but not October! Sugden begin by assuming that the arithmetic mean is equal to the general issue that your raises. S why I ’ m hoping for is some insight on whether to bother under. Gon na do is another approach to decision-making under uncertainty real-time fluctuations in electricity prices spatially variable and the are! Specified amount of protection against price spikes the arithmetic mean is equal to the risks of dollar rupee... Liked it Apr 10, 2020 to refinance have no way to consider rational decision under... Sibilities/Necessities, complete ignorance, small samples, etc illustrated by considering the likelihood for tail events Schedule ). Think I ’ m not sure what to do this analysis two formulations are equivalent from this distribution and the. Figure it out, or equity advantages can actually understand and use the.

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