Notes from talk “Ecology, Complexity, and Metaphor”

1. Notes from talk “Ecology, Complexity, and Metaphor” • Simon Levin’s talk • Theoretical ecologist (below directly from his web page) • understanding how macroscopic patterns and processes are maintained at the level of ecosystems and the biosphere, • In terms of ecological and evolutionary mechanisms that operate primarily at the level of organisms. • Examples: • evolution of diversification, • the mechanisms sustaining biological diversity in natural systems, and the • implications for ecosystem structure and functioning. • The work integrates empirical studies and mathematical modeling

2. Some integrating concepts • Both speakers unsure of “new science” idea or “complexity theory” concept • Multi-cellularity and human tribes (anthropology) • Evolution and Game-theory (from economics) • Spatial Stochastic Processes

3. Spatial Stochastic Processes • Perhaps combine Simulation with ESDA one day…test for pattern similarity • Certain CA rule sets manifest in patterns approximated by, say a SAR, MA or CAR process • {DGP is y=(I-pW)e} • Regression estimation is y*=pWy. • Directly estimating process is intractable… • ESDA tests as a way of estimating scalar parameter describing spatial autocorrelation within a cross-section of data • perhaps artifact of a complex adaptive system… so further analysis than say OLS should be performed • Reveals that units of study should not be though of as independent observations (most econometrics, traditional statistics)

4. Do communities/ecosystems exist? Clements Gleason Whittaker oz.plymouth.edu

5. R.H.Whittaker

6. Modeling Complexity: the limits to prediction (Batty and Torrens 2001) • Critical of Complex Systems Modeling My summary of their critique: Unless they can be validated with data then they are can not be considered useful— perhaps paper is a reaction to recent popularity of complexity modeling best illustrated with recent book of Wolfram? • From Forrestor to CA simulation models • Lack of testing or ability to test model vailidity empirically, with data not used for its calibrartion and formal statistical tests • I assume when they say ‘traditional modeling’ they mean mainly micro-economic theory specifying models and estimation and testing with a regression based econometrics approach

7. New complexity modeling doesn’t tell us anything new?: • “Much of complexity theory so far has, in fact, been concerned with demonstrating models of systems that were initially deemed inexplicable because they demonstrated surprising behavior. Once understood, this behavior is no longer surprising, but invariably it can only be explained by processes that exist at a micro level giving rise to phenomena at a macro level which, in turn, cannot be explained in traditional macro terms. In short, much of complexity theory and its modeling is rooted in explaining behaviors that have already been observed and in some sense, can thus be said to be no longer complex.”

8. Critical judgements towards Complexity Modeling • “Perhaps the most obvious use of complex systems models which generate unexpected change is for learning, education, and in the broadest sense for entertainment.” • 1)Parsimony • (? What about large scale structural econometric equations ?) • 2) Independence in validation • “ Models which cannot be validated are thus no different from qualitative reasoning, from intuition, or even dictat which were the usual schemes used to develop policy prior to the computer era.” • “…traditional norms of theory development and hypothesis testing have been relegated to the background.” • Idea that all they do is show many different possible outcomes originating from different combinations of rules. And no assumption of a process OR resulting prediction can ever be tested.

9. Parameterization:“Forrester strategy” • “The difference between complex systems models and those that appeal to the principles of strict parsimony . those that we have been referring to here as traditional models . is one that revolves around the explicitness of assumptions. In essence, traditional models are those in which all relations defining the model are testable (?) while complex systems models have chains of relations that are explicit but untestable in principle and/or untestable because data and observations of their processes are not available.” • Econometric not all is testable…sometimes not really sure what is being measured (measure = quantity x quality)…more difficult in economics to find measures of theoretical concepts • Proxy variables, mismeasurements, “steady-state” concepts, regime or dummy variables, human capital, utility etc… (for example in Barro Growth regressions) • Schelling Model EXAMPLE • “There are clearly examples of models of complex systems, such as the Schelling (1969) models of spatial segregation, which articulate local action that leads to global pattern in the simplest terms. However, even in that case, although the model is simple in its rules, observations of how individuals exercise their preferences to segregate are rarely available and the data to test such models is never complete.”

10. “...to see is to believe…” • (Mandelbrot 1983, p.21 context of fractal geometry) • ... The critical issue in complex systems models is that this is not the only strategy. There are many qualitative tests that are possible with respect to how plausible structures are which generate believable predictions, and these should be mapped out. In fact, there has been hardly any work whatsoever on strategies for validating models which deal with intrinsically complex systems, and one purpose of this paper is to raise awareness and encourage debate in this domain.

11. Demontrations NOT no practical value: • Their own experiments with CA model of devopment in Chicago • “…it is already clear that very different outputs can be obtained with quite minor changes in the rules themselves.” • “…such models are really demon-strations of systems principles rather than vehicles for operational analysis and policy-making. Models which might be built along these lines to demonstrate emergence and which are capable of being calibrated to real data are likely to be much more specific than those that we have illustrated here.”

12. a philosophical debate…? • different objectives, view points, personalities regarding the value of information and knowledge? • “The problem is that short of statistical or numerical criterion, good rules for choosing models based on a combination of discursive and reflective analysis as well as standard quantitative evidence are not well-developed. In the case of the CA-like models which we reviewed in the last section, there are so many assumptions about the representation of space and the nature of the transition rules that are used to determine development that it is not possible to definitively use such a model to make predictions that we can act upon.” Note: ? Perhaps we should never”act upon” the predictions a few people. And of a model the people that will be most affected cant understand…perhaps should just be used as one piece of information in a larger debate…and most accurate predtions are based on consensus and social choice which include risk and loss functions of all..