Since before the time of Aristotle and the natural philosophers, reductionism has played a foundational role in western scientific thought. The premise of reductionism is that systems can be broken down into constituent pieces and studied independently, then reassembled to understand the behavior of the system as a whole. It embodies the classical linear perspective. This approach has been successful in developing basic physical laws and especially in engineering where linear analysis dominates and systems are purposefully designed that way.

Here we examine a minimalist paradigm, empirical dynamics, for studying non-linear systems and a method that can distinguish causality from correlation. It is a data-driven approach that uses time series information to study a system holistically by reconstructing its attractor—a geometric object that embodies the rules of a full set of equations for the system. The ideas are intuitive and will be illustrated with examples from ecology, epidemiology and genetics.

Sugihara is a data-driven theoretical biologist whose work focuses on developing minimalist inductive theory to understand how systems work -extracting information from observational data with minimal assumptions. He has worked across many scientific domains including ecology, finance, climate science, gene expression, medicine and fisheries. Sugihara is most known for topological models in ecology; empirical dynamic forecasting models; first demonstrations of chaos in nature; research on generic early warning signs of critical transitions; methods for distinguishing correlation from causal interactions in time series; and has championed the idea that causation can occur without correlation.
</p>
<p>He is the inaugural holder of the McQuown Chair of Natural Science at Scripps Institution of Oceanography, UCSD and has been a two-term Member of the National Academies Board on Mathematical Sciences and their Applications; Member of NAS Committee to Evaluate US Fisheries Rebuilding; Member of NRC Committee to Evaluate the Effects of Sudden Climate Change.
</p>
<p>Sugihara was a Managing Director at Deutsche Bank (1996-2001) and was Head of Global Quantitative Proprietary Trading where his proprietary forecasting methods were used to manage bank investments. He is on the Advisory Boards of several companies and helped found Prediction Company (sold to UBS) and Quantitative Advisors LLC (an advisory company created by Deutsche Bank which leased Sugihara’s trading system until 2006). Sugihara has advised the Bank of England, the Federal Reserve Bank of New York, The European Central Bank and The United States Federal Reserve System on questions of systemic risk, and has appeared on panels regarding this topic with major government officials (see link below). In 2009 he was solicited for the position of Chief Scientist of NOAA at the level of Assistant Secretary Department of Commerce. His long-standing academic interest in out- of-sample forecasting, and in nonlinear (unstable, non-equilibrium) systems has relevance to various practical realms, where the implication of such behavior is large.