By Siobhan Bourke and Emily Lancsar
How can researchers interested in complex societal and environmental problems best understand and deal with uncertainty, which is an inherent part of the world in which we live? Accidents happen, governments change, technological innovation occurs making some products and services obsolete, markets boom and inevitably go bust. How can uncertainty be managed when all possible outcomes of an action or decision cannot be known? In particular, are there lessons from the discipline of economics which have broader applicability?
While uncertainty is often discussed alongside risk, a fundamental difference between uncertainty and risk is that risk involves events with known probabilities (or probabilities based on reliable empirical evidence), whereas under uncertainty probabilities are unknown and reflect an individual’s subjective belief concerning the likelihood of a given outcome. Given the subjectivity, that likelihood can differ from person to person. It can also involve a perceived zero probability in the case of unforeseen events (or ‘unknown unknowns’).
We highlight three approaches from economics that have broad value in managing uncertainty, especially for helping decision makers in taking uncertainty into account: expected utility theory, hedging, and modelling. A common strength of these approaches is that they explicitly consider uncertainty rather than ignoring it.
Expected utility theory
Expected utility theory provides a useful approach to choice under uncertainty. It helps decision makers think about different options in terms of the probabilities of those options occurring and to rank them.
In particular, expected utility is the utility an individual (or some aggregate of people) is expected to obtain under different circumstances or ‘states of the world’. It is calculated by taking the weighted average of all possible outcomes with the weights reflecting the probability that a specific event will occur. Decisions are made to maximise expected utility.
Expected utility theory is underpinned by an assumption of rationality. Extensions and alternatives to expected utility theory have been developed to account for lack of rationality in human behaviour and cognitive biases. A notable example is prospect theory (developed by Kahneman, a Nobel prize winner in economics, and Tversky, 1979) which describes how people choose between uncertain alternatives. It highlights the importance of reference points and recognises that individuals may view potential losses differently from gains.
Hedging can be used to manage both risk and uncertainty. Hedging is like insuring against an uncertain adverse outcome by offsetting potential losses associated with uncertain events by gains in other investments. This doesn’t prevent the negative (uncertain) event occurring, but reduces the adverse impact of that event, should it occur.
This can be achieved by taking a portfolio approach such that decision makers diversify across investments or courses of action. As economists are fond of saying ‘there is no free lunch’; reducing the adverse outcomes of an uncertain outcome usually comes at a cost of a reduced rate of return in certain times for increased survival in adverse times.
Modelling―especially systemic representations of complex real-world scenarios and simulations that account for the subjective probability of such scenarios occurring―provides a way of assessing existing (and projecting future) inputs and outputs and systematically testing the impact of policies in ways that include and account for uncertainty. Modelling is used not only in economics, but also in a wide range of other disciplines and fields as attested by multiple contributions to this blog (see https://i2insights.org/tag/modelling/).
What economics particularly brings to modelling is a basis in economic theory which is fundamental both to the building of the models and interpretation of the results along with focus on causal relationships and the explicit inclusion of uncertainty. A specific example is the development of decision analytic models to explore the cost effectiveness of health technologies (including drugs, devices, services, etc.) to inform government resource allocation decisions. Such models are used to reduce uncertainty regarding the question of value for money and guide government investment decisions.
Do you have other lessons from economics to share? Have you applied economic approaches to uncertainty outside the discipline? Are there issues in dealing with uncertainty that economists could usefully apply themselves to?
Kahneman, D. and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47: 263-291.
Biography: Siobhan Bourke PhD is a research fellow in the Department of Health Services Research and Policy, Research School of Population Health, The Australian National University in Canberra, Australia. Her research has focused on economic evaluations, orphan drugs policy, health policy and patient reported outcomes.
Biography: Emily Lancsar PhD is a Professor and Head of the Department of Health Services Research and Policy in the Research School of Population Health at The Australian National University in Canberra, Australia. Her broad research interests are in health economics, with a particular interest in understanding and modelling choice, preferences and behaviour of key decision makers in the health sector. Emily also leads the recently created ANU Health Policy Lab, premised on engagement and co-creation of research with policy makers and practitioners with the ultimate aim of contributing to improved health and wellbeing.
Siobhan Bourke and Emily Lancsar are members of blog partner PopulationHealthXchange, which is in the Research School of Population Health at The Australian National University.