Deep uncertainty

Deep uncertainty (50)
Epistemic uncertainty (12)
Fundamental uncertainty (4)
Great uncertainty (5)
Uncertainty (741)

Synopsis: Uncertainty: Deep uncertainty:

what they term deep uncertainty represented by very large scenario sets, sometimes over periods of hundreds of years 50.

Deep uncertainty characterizes many domains of decision-making in science and technology. In particular, under deep uncertainty, there is little agreement or consensus about system structure.

Thus, exploratory modeling is used to explore Technological forecasting & Social Change 76 (2009) 1138 1149 E-mail address:

Making better decisions under conditions of deep uncertainty does not only require rigorous analysis, but also political will

Decision-making under conditions of deep uncertainty is a consequence. Scenario planning has been developed as a method to represent

and deal with such deep uncertainties. Over the recent decades, it has formed a growing area of interest on the interface of academia and public and private sector policy-making.

Making better decisions under conditions of deep uncertainty requires not only rigorous analysis. Even well-constructed, thoroughly analysed scenarios can be of little use and relevance,

forks in the road and deep uncertainties that keep executives awake at night. In this context, the definition for drivers of change was decided to focus on things that are accessible

two of them are subcategories of so-called‘‘deep uncertainties.''''The latter ones are similar to the third category that Sven Ove Hansson (1996) has added to the discussion of uncertainty.‘‘

Walker, E w.,Marchau, V. and Swanson, D. 2010),‘Addressing deep uncertainty using adaptive policies: introduction to section 2'',Technological forecasting and Social Change, Vol. 77, pp. 917-23.

11 C. Hamarat, J. H. Kwakkel, E. Pruyt, Adaptive Robust Design under deep uncertainty, Technol. Forecast.

12 J. H. Kwakkel, E. Pruyt, Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty, Technol.

Adaptive Robust Design under deep uncertainty Caner Hamarat, Jan H. Kwakkel, Erik Pruyt Delft University of Technology policy Analysis Department, PO BOX 5015,2600 GA Delft

In this paper, we propose an iterative computational model-based approach to support adaptive decision-making under deep uncertainty.

it is also appropriate for any long-term structural and systematic transformation characterized by dynamic complexity and deep uncertainty. 2012 Elsevier Inc. All rights reserved.

Model-based decision support Deep uncertainty Adaptive policy-making Exploratory Modeling and Analysis 1. Introduction Conceptual, formal, and computational models are used commonly to support decision-making

i e. in case of‘deep uncertainty',then traditional modeling and model-based policy-making tends to fail. Deep uncertainty pertains according to Lempert et al. 8 to those situations in

which analysts do not know, or the parties to a decision cannot agree on, (1) the appropriate conceptual models which describe the relationships among the key driving forces that shape the long-term future,(

Deep uncertainty pertains, in other words, from a modelers'perspective to situations in which a multiplicity of alternative models could be developed for how (aspects of) systems may work,

It is clear that there is a strong need for policy-making approaches that allow for dealing with deep uncertainty,

In this paper, we propose an iterative model-based approach for designing adaptive policies that are robust under deep uncertainty.

and more generally, decision-making under deep uncertainty. The rest of the paper is organized as follows. Section 2 introduces an adaptive policy-making framework and our Adaptive Robust Design approach.

the adaptive policy-making framework and the Adaptive Robust Design approach 2. 1. The adaptive policy-making framework Under deep uncertainty,

This paradigm holds that, under deep uncertainty, policy-making needs to be dynamic with built-in flexibility 8, 15,20 24.

In a recent special issue of Technological forecasting and Social Change on adaptivity in decision-making, the guest editors conclude that Adaptive policy-making is a way of dealing with deep uncertainty that falls between too much precaution and acting too late.

support the development of long-term strategic policies under deep uncertainty, and test policy robustness over. EMA could also be used to develop adaptive policies under deep uncertainty

since it allows for generating and exploring a multiplicity of plausible scenarios by sweeping multidimensional uncertainty space.

Energy transitions are characterized by many deep uncertainties related to transition mechanisms, to the various competing technologies, and to human and organizational decision-making 45.

the adaptive policy is a better guarantee for a successful transition under deep uncertainty, without forcing a transition to new technologies upon situations that do not require a transition to take place (e g. in case of a cheap and environmentally friendly dominant technology) or for

under deep uncertainty. The proposed approach has been illustrated on an energy transition case. Several of our findings warrant further discussion.

a staged exploration of complexity and deep uncertainty, in: The 29th International Conference of the System Dynamics Society, WASHINGTON DC, USA, 2011.4 W. Walker, P. Harremoës, J. Rotmans, J. Van der Sluijs, M. Van

Parameter and State Estimation, Wiley Interscience, London, 1974.7 W. E. Walker, V. A w. J. Marchau, D. Swanson, Addressing deep uncertainty using adaptive policies:

a strategic analysis under conditions of deep uncertainty, in: Technical Reports, RAND, Santa monica, California, 2009.14 D. H. Meadows, J. Richardson, G. Bruckmann, Groping in the dark:

a promising method to deal with deep uncertainty, in: Technology policy and Management, Delft University of Technology, Delft, 2008, p. 285.37 E. Pruyt, J. Kwakkel, A bright future for system dynamics:

W. L. Auping, E. Pruyt, Dynamic scenario discovery under deep uncertainty: the future of copper, Technol.

His research interests are exploration and analysis of dynamically complex systems under deep uncertainty. In his Phd research, he focuses on long term decision-making under deep uncertainty using the Exploratory Modeling and Analysis method.

His applied interests include climate change/energy issues, public health and health policies, financial crisis and energy systems. His current research interests are adaptive policy making and the use of optimization in policy-making.

Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty Jan H. Kwakkel, Erik Pruyt Faculty of technology, Policy,

Future-oriented technology analysis Exploratory Modeling and Analysis Deep uncertainty System dynamics Adaptive policymaking Agent-based modeling 1. Introduction Future-oriented technology analysis (FTA) is understood as an umbrella label for various approaches

In other literature, this is called deep uncertainty 7, 8, or severe uncertainty 9. It can be understood as a situation where one can incompletely enumerate multiple possibilities without being able

It thus explicitly addresses one of the FTA challenges identified by Porter et al. 1 by assessing how EMA could contribute to adaptive foresight 10 under deep uncertainty.

the challenges associated with decision-making under deep uncertainty can largely be overcome. Instead of trying to predict,

In this way, decision-making can proceed despite the presence of deep uncertainty, for decisions can be designed to be robust across the explored range of possible futures.

Under conditions of deep uncertainty, long time horizons, and high dynamic complexity, a more exploratory use of models is called for 26.

since deep uncertainty does not warrant probabilistic interpretations. 423 J. H. Kwakkel, E. Pruyt/Technological forecasting & Social Change 80 (2013) 419 431 otherwise.

and implications for FTA This paper started from the observation that model-based decision support under conditions of deep uncertainty is problematic.

EMA addresses the problem of deep uncertainty by systematically exploring over the uncertainties, potentially resulting in an information overload.

A Promising Method to Deal with Deep uncertainty, in: Faculty of technology, Policy, and Management, Delft University of Technology, Delft, 2008.14 J. H. Miller, Active nonlinear tests (ANTS) of complex simulation models, Manag.

dynamic scenario discovery under deep uncertainty, Technological forecasting and Social Change,(under review. 23 R. U. Ayres, On the practical limits to substitution, Ecol.

H. Kwakkel, E. Pruyt, Adaptive Robust Design under Deep uncertainty,(under review. 45 G. Yücel, Analyzing Transition Dynamics, Delft University of Technology, Delft, 2010.46 L. Breiman, J. H. Friedman, C. J. Stone, R. A

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