Managing innovation dilemmas: Info-gap theory

By Yakov Ben-Haim

Author - Yakov Ben-Haim
Yakov Ben-Haim (biography)

To use or not to use a new and promising but unfamiliar and hence uncertain innovation? That is the dilemma facing policy makers, engineers, social planners, entrepreneurs, physicians, parents, teachers, and just about everybody in their daily lives. There are new drugs, new energy sources, new foods, new manufacturing technologies, new toys, new pedagogical methods, new weapon systems, new home appliances and many other discoveries and inventions.

Furthermore, the innovation dilemma occurs even when a new technology is not actually involved. The dilemma arises from new attitudes, like individual responsibility for the global environment, or new social conceptions, like global allegiance and self-identity transcending all nation-states. Even the enthusiastic belief in innovation itself as the source of all that is good and worthy entails a dilemma of innovation.

An innovation’s newness and the uncertainty of its promise for improvement is the source of the dilemma. Tomorrow we will understand the innovation better, its dangers and its benefits, but today we must decide. Without our optimistic belief in the future we would remain always in the past. But optimism without understanding can be dangerous because the innovation may harbor unanticipated and unpleasant surprises. We need a sensible and responsible method for responding to the endless flow of discovery and invention.

Info-gap theory provides the basis for such a method. Central to the theory is the idea of an information gap: the disparity between what you do know and what you need to know in order to make a responsible decision. Info-gap theory explores the link between the boundlessness of our ignorance and the limitation of our ability to achieve optimal outcomes. Indeed outcome optimization becomes less feasible as uncertainty grows.

The main info-gap approach to managing an innovation dilemma is to make a decision that robustly satisfies critical requirements. The idea is to identify outcomes or consequences that are essential (that must be achieved) and then to choose the alternative that will achieve these outcomes over the widest possible range of potential realizations.

Focusing on reliable achievement of critical goals is different from focusing on achieving the best possible outcome. The considerations differ (and the outcomes may or may not differ). Focusing on optimal outcomes ignores the central importance of reliably achieving critical results in the face of deep uncertainty. In contrast, focusing on robustness against ignorance, while aiming at specific critical goals, enables decision makers to balance the quality of the outcome against the confidence in achieving an acceptable outcome.

Info-gap evaluation of robustness is based on three components: system model, uncertainty model, and performance requirements. The system model may be quantitative or qualitative, and represents the situation of interest, where some parameters or functions are uncertain. The uncertainty model quantifies the uncertainty non-probabilistically, and the horizon of uncertainty is unbounded – there is no known worst case. The performance requirements are those outcomes that must be achieved in order for the decision to be acceptable. The robustness of a proposed decision is the greatest horizon of uncertainty up to which the system model is guaranteed to achieve the specified performance requirements.

In summary, the info-gap conception of an unknown unknown entails two ideas. First, we don’t know the probabilities of alternative realizations, or at best we know probabilities only very imperfectly. Second, we don’t know how wrong our current understanding is. More precisely, there is no known worst case or, stated pessimistically: if you think things are bad now, they could be much worse. Info-gap theory proposes to manage unknown unknowns by first identifying essential outcomes, and then by prioritizing the decision alternatives in terms of their robustness to uncertainty for achieving those critical outcomes. Rather than aiming at optimal outcomes, when facing deep uncertainty we attempt to achieve necessary outcomes over the widest range of surprise.

Everyone faces deep uncertainty – though we may give it different names: unknown unknowns, info-gaps, severe uncertainty. What deep uncertainties have you faced, and how have you dealt with them? What seems to work, and under what sorts of circumstances?

To find out more:
Ben-Haim, Y. (2018). The Dilemmas of Wonderland: Decisions in the Age of Innovation. Oxford University Press: Oxford, United Kingdom.

See also:
Ben-Haim, Y. Info-Gap Theory: Decisions Under Severe Uncertainty. Technion Israel Institute of Technology, Haifa, Israel. (Online):

Biography: Yakov Ben-Haim PhD initiated and developed info-gap decision theory, a decision-support tool for assessing and selecting policy, strategy, action, or decision in a wide range of disciplines and when facing deep uncertainty. He holds the Yitzhak Moda’i Chair in Technology and Economics at the Technion – Israel Institute of Technology in Haifa, Israel.

This blog post belongs to a series on unknown unknowns as part of a collaboration between the Australian National University and Defence Science and Technology.

Published blog posts in the series:

Accountability and adapting to surprises by Patricia Hirl Longstaff

How can we know unknown unknowns by Michael Smithson

What do you know? And how is it relevant to unknown unknowns? by Mathew Walsh

Scheduled blog posts in the series:

October 22: Creative writing as a journey into the unknown unknown by Lelia Green
November 5: Looking in the right places to identify “unknown unknowns” in projects by Tyson R. Browning
November 19: Blackboxing unknown unknowns through vulnerability analysis by Joseph Guillaume