Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - PICS (Prediction in Complex Systems)

Teaser

The overall purpose of the research project is to provide a new framework for understanding and evaluating predictions in complex systems. The framework focuses on three related questions: First, why do scientists in some sciences face disproportionate difficulties in making...

Summary

The overall purpose of the research project is to provide a new framework for understanding and evaluating predictions in complex systems. The framework focuses on three related questions: First, why do scientists in some sciences face disproportionate difficulties in making precise and accurate predictions? Second, what are some different roles that predictions can play in theory and practice? Third, given the previous two questions, are there different ways in which certain predictions should be evaluated? These questions have been investigated from the standpoint of Ecology. A revised notion of \'Ecological complexity\' explains the difficulties of prediction. This is important not just for philosophical and ecological research, but also because it has implications beyond academia. Failed predictions can place life, property and the environment at risk and thus create and perpetuate a situation of mistrust between scientists and other stakeholders, including the general public. Solving the problem of prediction can help to mitigate these issues.

In philosophy of science, especially in the literature on modelling, complex systems are usually understood as those that have many interacting parts (this is the current state of the art). At first glance, this conception works well for ecological systems. However, closer examination reveals that this complexity does not explain the problems faced by ecologists, namely the difficulty in making generalizations and accurate predictions (Matthewson, 2011). These difficulties are only explained fully if we take into account the causal heterogeneity of ecological systems, i.e. the causes of ecological phenomena vary across space and time. For example, a Boeing 747 is a complex system, yet each Boeing 747 is very similar to all the other Boeing 747s. In contrast, a marine ecosystem and a forest ecosystem might have similar trophic levels, but the entities in each level are different and behave differently. Complexity can but need not contribute to causal heterogeneity.

Causal heterogeneity leads to predictive failure. More specifically, the process of generalizing usually involves omitting factors that are particular to each instance of a phenomenon so as to focus on what is common between the various instances. If the systems in question are causally homogeneous, then omitting details from the model is usually not problematic. That is, if the causal factors of the phenomenon are the same across systems, then we only need to identify these and include them in our models in order to have an accurate causal picture of the phenomenon. However, in cases of causal heterogeneity, the differences between systems are relevant causal factors, not mere details. The ‘idiosyncrasies’ of each system, i.e. the aspects of a system not shared by other systems, are not irrelevant details, but factors that affect the functioning of the system. Predictions are based on patterns. Scientists make predictions for the behaviour of a system, based on the past behaviour of that system or the current behaviour of a similar system. Causally heterogeneous systems behave differently across space and time; hence predictions are difficult to make and have low chances of success.

Work performed

The main achievement of the project so far is the development of a new framework for understanding \'complexity\' and ‘prediction’ in applied science.

1. Papers:
- Elliott-Graves, A. The Value of Imprecise Predictions (forthcoming at Philosophy Theory and Practice in Biology)
- Elliott-Graves, A. The Future of Predictive Ecology (under review at Philosophical Topics)
- Elliott-Graves, A. Review of Defending Biodiversity: Environmental Science and Ethics by Newman J., Varner G., & Linquist S., (2017), Cambridge University Press. Found at: Notre Dame Review of Books https://ndpr.nd.edu/news/defending-biodiversity-environmental-science-and-ethics/
- Elliott-Graves, A. ‘Prediction in Science’ – proposal accepted at Philosophy Compass

2. Monograph:
- Ecological Complexity - Under contract with Cambridge University Press

3. Selected Conference & Workshop Presentations:
- August 2019 – Philosophy of Biology on Dolphin Beach 13, Moruya, Australia. Title of Paper: Prediction in the Wild (invited speaker)
- July 2019 – Philosophical Perspectives on the Niche, Münster, Germany. Title of Paper: “Niche concepts, Species Distribution Models and Biological Invasions” (invited speaker)
- July 2019 – International Society for the History, Philosophy and Social Studies of Biology, Oslo, Norway. Title of Paper: “Meta-analysis as a Predictive Tool for Invasion Biology”, in the Symposium that I organised: “Tackling Bioinvasions 60 years on: lessons from the trenches”. (refereed presentation)
- June 2019 – Charles University. Idealization Across the Sciences Workshop, Prague, Czech Republic. Title of Paper: Agreeing to Disagree: Pluralism about Optimal Model Complexity (invited speaker)
- June 2019 - Australian National University, Australia. Workshop: Scientific Modelling: Pushing the boundaries. Title of Paper: Agreeing to Disagree: Pluralism about Optimal Model Complexity (invited speaker)

4. Teaching
- Prediction in Complex Systems. Foundations Seminar Series (PhD level seminar), Australian National University. Southern Hemisphere Fall 2019.

5. Other
- Interdisciplinary workshop titled: Epistemic and Non-Epistemic Values in Ecology
- Philosophy and Biology Reading group on \'Extended Heredity\' at the Australian National University (participant)

Final results

This project has far-reaching implications for philosophy of science and scientific practice. First, it shows that the traditional approach to improving predictions, popular in philosophical circles in the past but still prominent in many scientific disciplines, is unlikely to work in the case of complex, heterogeneous systems. This approach entails the creation of more general, unifying theoretical frameworks, under which larger numbers of particular phenomena can be subsumed. Yet if the systems are heterogeneous, then the process of generalizing involves omitting the very causal factors that are necessary to make accurate predictions in particular systems. This leads to the second implication, namely that very simple, general models will be of limited use for making accurate predictions in these types of systems, whereas more complex models will have a higher chance of predictive success.

The ability to make timely and accurate predictions is expected of applied scientists by their peers, their employers and the general public. Failed predictions can place life, property and the environment at risk. Therefore, the issues of trust, credibility and accountability are of primary importance for the public understanding and appreciation of scientific research and intertwined with the feasibility of the research itself. I believe that philosophers of science, especially those who have empirical knowledge in more than one field, are well suited (and consequently duty-bound) to mediate the dissemination of scientific results not only to other researchers, but also to administrators, policy-makers, non-academic stakeholders and the general public. To this end, the second year of the project will focus on examining the best way of representing scientific results, especially those that involve uncertainty, to extra-academic stakeholders, including policy-makers and the general public.

Website & more info

More info: http://alkistis-elliott-graves.net/.