Studying complex issues at the interface of humanity and the planet meansthat we need to relate to ‘wicked’ and ‘messy’problems that are driven by intricate causal relations, correlations andcomplex feedback loops. Ensuing from constantly changing environments, theyare characterised as value-laden, open, multi-dimensional, ambiguous,unstable, uncertain and unpredictable. Still, we would hope that scienceenables us to develop knowledge that is robust enough to help find solutionsto these rather persistent issues. In this chapter we will find out whetherand how this can be done by reflecting on the implications of complexitythinking for the scientific research practice.

We start with a short recapitulation of the various functions that theavailable types of research can perform. Then we review how they can becombined and integrated to meet the demands of complexity thinking, and whatthis requires of science. We devote special attention to the question of howresearch projects can be designed in such a way as to enhance the engagementof scientific researchers and other stakeholders in real-lifecomplexity.

Next, we address whether and under what conditions we can maintain the claimthat science leads to societal progress. Arguing that the traditionalstandards for scientific knowledge are not suited to assess the knowledgeprocesses involved in inquiries into ‘wicked’ problems, wereflect on what could be regarded as more adequate quality criteria forpresent-day science.

In the conclusion, we evaluate what this all means for the institutionalmake-up of society and for researchers who are engaging in projectsconcerning ‘wicked’ problems. We summarise the types ofknowledge they need to acquire and the kind of skills they need to developto be able to deal with complexity.

Towards a Complexity-Based, Integrated Research Approach

In mode 1, the standard method is the leading model. Research projects arepreferably set up as empirical or modelling cycles. Via systematic research,increasingly sophisticated theories are constructed from which hypothesesare deduced or projections and simulations are designed that are tested byempirical experimentation, model runs, statistical inference andmathematical computation.