Why do we study the future?

‘It is possible to be … surprised, and at the same time be … prepared’

‘Chance favors the prepared mind’ . (Grabo, 2012)

In our opinion these two statements best represent why an organization would want to invest in future studies. They also highlight a question which is amenable to serious and meaningful scientific research: “What do we have to do to be best prepared and least surprised?”. This sites summarized our effort in this direction.

Ocean Futures

Ocean Futures is a strategic project funded by Oceans and Atmosphere’s MRI program, with inclusion of staff from other programs and BU’s. The goal of the Ocean Futures project is to develop new insights, approaches, understanding and tools that can help marine scientists, managers and policy makers prepare for future challenges.

Our work is based on five approaches and four concepts. These are described briefly below and more at length in the linked pages.

Download a summary of the OceanFutures work (2 page, PDF): [ddownload id=”692″]

The approaches

To achieve the project’s goals, the team uses a variety of approaches, including foresighting, super-forecasting, quantitative and qualitative modelling, historical data analysis and workshops and surveys.

  1. Foresighting is concerned with futures that are usually at least 5-10 years away. It draws on approaches used in long-range and strategic planning, horizontal policymaking, democratic planning, and participatory futures studies. The team uses foresighting approaches to prepare for alternative marine futures, and ultimately seek to contribute to over-the-horizon strategic planning for O&A.
  2. Super-forecasting aims to provide probabilistic forecasts of specific events in the near future (at most a few months away) and to develop forecasting skills by tracking the accuracy of the forecasts. The approach is based on the belief that better forecasting leads to better decision making and on the empirical results that forecasts can be improved with behavioral interventions.
  3. Historical data analysis is motivated by the obvious fact that insight into the future can be gleaned from an understanding of the past and that what we empirically know has necessarily been acquired by looking at the past.
  4. Quantitative and qualitative modelling is an excellent means of synthesizing information and understanding. Models have an important role in planning for the future where conditions may depart from those experienced in the past, a range of options are possible, and goals can vary.
  5. Workshops and surveys are crucial to understand how people, including researchers and managers,  relate to and view the future, what their expectations and fear are, and how these affect their decision making. They provide insight into perspectives and expectations from different stakeholder groups which are critical in considering and evaluating alternative futures.
  6. Scenarios and Projections are different approaches used to say something meaningful about the future. By analysing the extent to which the futures as imagined by these approaches align and, when they do not, why it is so, we seek to provide a framing for future thinking as applicable to Australia’s oceans which can assist long-term policy and planning integrated across sectors.

The concepts

1. There is a profound difference between foresighting and forecasting.

  • Foresighting is about a future reasonably far from now. It requires imagination, broad knowledge and a reasonable propensity towards intellectual adventure. Feedback is provided mostly in terms of intellectual counter arguments. ‘Data’ mostly comes in the form of trends, often fuzzy and contradictory. Evidence, mostly in terms of indicators, is never conclusive and often amenable to alternative interpretation
  • Forecasting is about a future next to now. It requires both broad and expert knowledge and the courage to accept being proved wrong. ‘Data’ also comes in the form of trends, often fuzzy and contradictory. But evidence and feedback are provided by reality.

In an imaginary time line pointing towards the future, forecasting and foresighting are located in two disjoint locations (see figure below). Our previous work (Boschetti et al., 2016a; Richert et al., 2017) and a vast literature  (see review in (Boschetti et al., 2016b)) suggest that forecasting and foresighting may involve very different cognitive processes. As a result, we may speculate that they may also require a very different set of skills and may be prone to different sets of biases.

The Super Forecasting exercises in the relation to the Foresights exercises, in an imaginary plane with (future) time in the horizontal axis and scale-scope in the vertical axis

2. We cannot study the future, but we can study how people relate to and think about the future.

The way people think about the future affects their behaviour, which in turns can affect the future. This is particularly true for decision making in environmental problems, in which human behaviour is the main driver of change. We have carried out extensive research in this area (Boschetti et al., 2014; Boschetti et al., 2017; Boschetti et al., 2016a; Boschetti et al., 2016b; Boschetti et al., 2016c; Richert et al., 2017). Two main results stand out:

  • People tend to imagine five types of futures, which we call Myths of the Future, (Boschetti et al., 2016b): ‘Social-Crisis’, ‘Eco-Crisis’, ‘Techno-optimism’, ‘Power and Economic Inequality’ and ‘Social Transformation’. The Social Crisis myth describes beliefs that traditional values, social order and human competence are likely to decline in the future. The Eco-Crisis myth describes beliefs that environmental conditions and natural habitats are likely to decline and lead to social unrest. The Techno-Optimism myth describes beliefs that science, and technology are likely to create innovations which improve quality of life. The Power and Economic Inequality myth describes beliefs that big business and governments are likely to become more powerful and cause social inequality and economic crisis. Finally, the Social Transformation myth describes beliefs that society is likely to become more decentralized, caring and collectively empowered. Each of these myths can be seen as a lens through which expectations and fears about the future are formed and which thus determine choices and supports for different types of actions and policies.
  • When decisions fail to properly account for future outcomes, usually we assume this is due to poor system understanding or short-sighted incentives. One of our results (Richert et al., 2017) suggests that other factors may be at play. In some cases not one, but two mental models are employed in the decision making process: one describes how the system works today and which choices are preferred and one describes how the system will work in the future and the expectations of the outcome of our actions. Crucially, these two mental models may not be consistent, so that the choices made according to the first mental model do not lead to expectations defined according to the second mental model. Computer models can be essential to clarify these mismatches (see below).

3. Models are effective tools in future studies.

Our overall team has an extensive expertise in the use of computer models in understanding socio-ecological and bio-physical systems and in providing advice to decision makers based on the results of such models. Reference to this very diverse work can be found by checking out the publications of our team members. In addition, we have carried out some work on the role of computer models in the decision making process.

Effective decision-making involves prediction, since without some approximate guess at the consequences of available options, there would be no reason to choose one decision over another. This leads us to ask what type of prediction best supports decision-making. An extensive body of work shows that humans, including very bright individuals and experts, can be very poor at predicting how systems behave (Dorner, 1996; Sterman, 2008). Computer models can help because they provide predictions (Boschetti et al., 2011; Boschetti and Symons, 2011) which are in general more reliable than the ones even expert may produce.

They strength lays in proving a tool which integrates all available knowledge as well as accounting for uncertainty and missing information. We can be used to explore the possible consequence of different human decisions and actions (Boschetti et al., 2013), circumventing many of the well-known limitations of the human brain at handling complex dynamics and information.

4. Studying the past help understanding the future.

We all agree that knowing and understanding the past is crucial to say something meaningful about the future. We also typically assume that the past in necessarily better known or knowable that the future. However, this is not necessarily the case and the philosophical roots and implications of this issue are discussed in (Symons and Boschetti, 2013).  A paper discussing the implications of studying the past in order to make better decision in the field of environmental management is currently under preparation and will be made available as soon as possible.


Boschetti, F., Fulton, E., Bradbury, R., Symons, J., 2013. What is a model, why people don’t trust them and why they should, in: Raupach, M.R., McMichael, A.J., Finnigan, J.J., Manderson, L., Walker, B.H. (eds.), Negotiating Our Future: Living scenarios for Australia to 2050, vol. 2. Australian Academy of Science, Canberra, pp. 107-118.

Boschetti, F., Fulton, E., Grigg, N., 2014. Citizens’ Views of Australia’s Future to 2050. Sustainability 7, 222-247.

Boschetti, F., Gaffier, C., Moglia, M., Walke, I., Price, J., 2017. Citizens’ perception of the resilience of Australian cities. Sustainability Science 12, 345-364.

Boschetti, F., Gaffier, C., Price, J., 2016a. Citizens’ Perception of the Resilience of Australian cities. Sustainability Science submitted.

Boschetti, F., Grigg, N.J., Enting, I., 2011. Modelling = conditional prediction. Ecological Complexity 8, 86-91.

Boschetti, F., Price, J., Walker, I., 2016b. Myths of the future and scenario archetypes. Technological Forecasting and Social Change 111, 76-85.

Boschetti, F., Symons, J., 2011. Why models’ outputs should be interpreted as predictions, International Congress on Modelling and Simulation (MODSIM 2011). MSSANZ.

Boschetti, F., Walker, I., Price, J., 2016c. Modelling and attitudes towards the future. Ecological Modelling 322, 71-81.

Dorner, D., 1996. The Logic Of Failure: Recognizing And Avoiding Error In Complex Situations. Metropolitan Books, Ney York.

Grabo, C.M., 2012. Anticipating surprise: Analysis for strategic warning. Lulu. com.

Richert, C., Boschetti, F., Walker, I., Price, J., Grigg, N., 2017. Testing the consistency between goals and policies for sustainable development: mental models of how the world works today are inconsistent with mental models of how the world will work in the future. Sustainability Science 12, 45-64.

Sterman, J.D., 2008. Risk Communication on Climate: Mental Models and Mass Balance. Science 322, 532-533.

Symons, J., Boschetti, F., 2013. How Computational Models Predict the Behavior of Complex Systems. Foundations of Science 18, 809-821.