Panning Panarchy: an urban political ecology take on panarchy’s Ptolemaic planet

Panning Panarchy: an urban political ecology take on panarchy’s Ptolemaic planet

KeynoteEPSCoR All-Hands Meeting

Anchorage, Alaska14 May, 2009

KeynoteEPSCoR All-Hands Meeting

Anchorage, Alaska14 May, 2009

Tad ParkAnthropology

University of Arizona

AbstractAbstract

n An ecological model of adaptive cycles originated by Holling has been promoted recently (Gunderson & Holling, Panarchy 2002) as having the potential to model global ecological and even human processes. In this scaling up to the level of the globe, the modelers suggest that it is largely sufficient to imagine a series of interconnected adaptive cycles. Those of us who come from the social sciences find the idea that a model derived from simple ecological processes would scale up to global human systems less than persuasive. I will suggest that while I share this position it is worth taking a closer look at the panarchy model because this allows us to clarify the key differences between, for example, an urban political ecology model and an ecology model derived from plant studies. These two extremes highlight the range of phenomena that can be usefully modeled and the requirements of any sensible model of large cities forces us to consider a range of core philosophical issues from causality and complexity to sustainable development and political ecology. It should be no surprise that those who specialize in people have a critical view of extending flora and fauna models to human systems.

Intellectual OverviewIntellectual Overview

n A consideration of some key concepts:

n Causality

n Scale

n Emergence

n Ergodicity and nonErgodicity

n Panarchy as a model of global systems

n An urban political ecology relying on TGMs and ABM modeling

CausalityCausality

n Pre-Enlightenment Europe inherited from the Aristotelean tradition the idea that causes could be divided into four types: material, efficient, final and formal (e.g. a battle).

n For some time, post-Enlightenment scientists have tried hard to ignore the last two of Aristotle’s causes and pushed a positivist and materialist explanation of causality.

n If we begin with the shock quantum physical descriptions of reality (e.g. quantum flux) brought to Newtonian physics, it may be worth asking if nothing can regularly cycle into something why so many scientists continue to oppose the idea of emergent properties at higher scales.

n Some work in theoretical ecology (R. Ulanowicz 1997) has argued that while Aristotle’s four causes may not provide a sufficient or unambiguous description of causality, new ideas from quantum physics, chaos theory, and complexity theory have irrevocably damaged traditional reductionist explanations of causality.

Issues of ScaleIssues of Scalen Modelers need to ask a number of questions that are only partially

related:

n Are processes at multiple scales driven by different variables ?

n Are processes at multiple scales dissimilar in form ?

n Do economies of scale change anything fundamental about a system ?

n Are processes at one scale causally linked to those at other scales ?

n If we answer “yes” to the above in any particular example we should provide both theoretical and particular reasons.

n Extreme conceptual simplicity is not necessarily a virtue and some simplification may lead to both confuscation and complication, as the Ptolemaic system well illustrated.

EmergenceEmergencen The idea of emergence has appeared in dogmatic and nondogmatic

forms:

n The properties of this system cannot be explained from any amount of knowledge about its constituent elements.

n Some properties of the system may be new and not explainable by any amount of knowledge about its constituent elements.

n There are disagreements as well about what sort of causality might emerge: whether it is deterministic (e.g. some versions of deterministic nonlinear systems), autocatalytic (e.g. some biological systems) or, in the case of social scientists, new final causes (e.g. changing aspirations) or new formal causes (e.g.structural changes which constrain behavior).

n A lot of work in complexity theory focuses on systems with multiple basins of attraction (or multiple behaviors / strategies with attractive payoffs) and the idea that this multiplicity of limited optima can themselves evolve / adapt. From this point of view, what emerges may be a transformed system.

Ergodicity and NonergodicityErgodicity and Nonergodicityn “The nonergodicity of the universe as a whole and the biosphere in

particular is interesting from another point of view. History enters when the space of the possible that might have been explored is larger, or vastly larger, than what has actually occurred. Precisely because the actual of the biosphere is so tiny compared to what might have occurred in the past 4.8 billion years and because autonomous agents can evolve by heritable variations that induce propagating frozen accidents in descendent linages, the biosphere is profoundly contingent upon history.” (S. Kaufman, p. 152, Investigations, 2000).

n We might ask:

n How nonergoditic is a model ?

n Is this openness on the same order as that of human societies (e.g. with developed culture, technology, socio-economic organization) ?

n Are constraints and opportunities for innovation comparable in ecological systems and human societies ?

Panarchy as a model of Global Systems

The Adaptive Cycle

Panarchy as a model of Global Systems

The Adaptive Cycle

n Hollings et al argue that the basic model of an ecosystem is a figure eight plot conceived as an adaptive cycle with four phases (exploitation, conservation, release and reorganization) associated with the bottom left, upper right, bottom right and upper left quadrants respectively.

n The plot places potential on the Y-axis and system connectedness on the X-axis).

n The adaptive cycle imagines a simultaneous increase in resource incorporation and system organization culminating in a highly productive state that then becomes rigid and maladaptive leading to decline first in productivity and then in system organization. This process of decline leads to rapid loss of system organization and simultaneous increases in productivity within a simpler less complex system followed by its decline and reorganization into more complex systems giving rise to another cycle.

n Earlier ecologists had a logistic model that suggested small rapid growth species [r] might be replaced by slowly growing larger species [K] (e.g. large trees replacing quick growing opportunists).

n The Hollings model added two terms to turn the model into an adaptive cycle.

n The model depicted above has now been modified in two ways:

n 1) to add a third dimension (resilience) to make the argument that some parts of the cycle are more resilient to perturbation than others (e.g. low connectedness and high resilience foster creativity [α] but a perturbed system with such high connectedness as to be brittle [Ω] has little resilience and perturbations clear the way for later creativity.

n 2) the combination of multiple adaptive cycles into a global system.

PanarchyPanarchy

A closer lookA closer look

n Basic characteristics of the panarchy model include:

n Figure eights all the way up and down, any flow in the system is allowed as long as it follows a figure eight.

n Sustainability is equivalent to keeping four phase adaptive cycles interlinked: even up to the level of human socio-economic systems.

n Each adaptive cycle can have autocatalytic properties and influence the figure eight above or below it in scale via the nonlinear dynamics of a set of 3-5 key variables.

n Scale in time (periodicity) is tied to scale in space: the model has no room for long term trends.

n Causality is not bottom up (i.e. not reductionistic) but the global system has limited nonergodicity: creativity and openness are reduced by scaling characteristics, constraints on cycle flows and basic structural features.

Reasons for skepticismReasons for skepticismn A cyclical version of history is an old idea but not a persuasive one these

days.

n The extension of an adaptive cycle model to modern global social systems is unpersuasive.

n We have ample evidence of trends in increasing consumption and impacts on the environment since the industrial revolution yet, the adaptive cycle provides few clues to why this should be.

n In a seriously nonergodic world, there are likely to be complex causal influences including chaotic ones in which very small changes beget very large ones and it is not clear that key variables come in finite packets like keystone species.

n Human culture and social organization have provided tools that allow rapid and major transformations of types not found in simpler ecosystems so the panarchy model is a bit like insisting that since a circle is perfect the solar system can be modeled as a series of circles: given enough circles the approximation was close but the model lacked any real sense of causality.

n Even economists these days have recognized human behavior is social and not simply egoistic, anthropologists have known this since the 19th century.

An alternative adaptive cycleAn alternative adaptive cycle

n From R. Ulanowicz (Ecology, the ascendent perspective, 1997) p.90. Growth in biomass and complexity go hand in hand except for a very brief brittle period before the crash.

n Mutual information of flow structure refers to the information content of the flow structure.

n The path, derived from the theoretical ecology of estuaries, seems more realistic for complex systems and information theory is a useful way to bridge the study of ecosystems and social systems.

An urban political ecologyAn urban political ecologyn Desiderata:

n To understand the differential situation poor in the rapidly growing cities of the world confront.

n To model large urban areas for which minimal socio-economic information is available in such a way as to be able to address key economic concerns such as allocative efficiency and key socio-political concerns such as distributive justice which are both implicated in development traps.

n To have a model which is modern in the sense of recognizing the evolutionary (S. Bowles Microeconomics, 2004) or transformational (E.J. Nell, General Theory of Transformational Growth, 1998) character of capitalist society and the multiplicity of commitments individuals have (Sen Rational Fools, 1977).

n Ideally, to have a model in which we can examine policies such as SAPs and consider their differential impact across the city in terms of both efficiency and distribution.

n Minimally, to have a model that raises key questions and possibilities that may otherwise be overlooked.

Trends in Global PovertyTrends in Global Poverty

n From World Development Report 2006: Measuring those with at least $1 per day. The orange line represents no change between 1981 and 2001, below it represents a decrease in poverty while above it represents an increase.

Different segments experience different vectors of change

Different segments experience different vectors of change

n From World Development Report 2006.

n The graphic makes it clear that, while the economy in both Tunisia and Senegal grew comparably, in Tunisia the poor received the bulk of the benefit while in Senegal the poor did poorly and the wealthy did well.

Global trends in urbanizationGlobal trends in urbanization

n World wide there is an astonishing increase in cities with over a million inhabitants but almost all of that increase in in the poorer countries of the globe.

n Development traps (e.g. poor education, disease, drugs, and crime) can prevent many in urban poverty from benefiting from potential urban advantages.

The growth of megacitiesThe growth of megacitiesn Cities with over 12 million inhabitants (by some estimates): China, U.S.,

India, (2 each), Brazil, Japan, Mexico, Nigeria, Argentina, and South Korea (1 each).

n Cities with more than 3 million inhabitants: Asia (31), North America and Caribbean (15), Europe & Russia (14), South America (13), North Africa and Middle East (10), sub Saharan Africa (6) Australia and Pacific (2).

n What counts as a single city is often a matter of municipal jurisdiction but the above figures amalgamate urban conglomerations and represent more or less what the eye would perceive as a city.

n Many cities of one million are now filling in rather small but propitiously located spaces and will soon connect up to form large continuous urban zones in the more populous countries.

n Urbanization may be seen as the outward appearance of growth in complexity (increases in information content) which translate at the level of human behavior into increases in strategy space and the number of viable options. Yet, the alternative potential for degradation and simplification, particularly in poor countries, suggests that we might envisage a range corresponding to that between a vibrant coral reef and a vast algal bloom.

Multiple basins of attractionMultiple basins of attractionn One of the advantages of an agent based model is that evolutionary

agents can potentially search opportunity space and discover a range of distinct and rewarding life strategies thereby illuminating complexity and to some degree illustrating the range of actual human behavior while also suggesting some of the key variables relevant to that range of behavior.

n Agents and models can be honed to focus on specific questions rather than attempting to explain all behavior in one model.

n In an early model based on data collected between 1998 and 2001 on an NSF grant (#9817743, #0138217) we were puzzled by the range in household size in cities such as Niamey and Bamako so we developed an ABM to address the issue of how likely it would be that households from 5-6 up to 45 members would all be viable given the socio-economic data we had.

n The model simply used three proxy variables: generosity (the willingness to have relatives reside with you), ambition (social mobility the model and five social strata with different income opportunities between which agents could move), education (a proxy for capacity to make more of any income earning opportunity). More household members and more education both increased costs and potential earning capacity. The key question addressed was how complex a model would have to be to not devolve into one strategy for household size.

Entropy measuresEntropy measures

While the model was reasonably realistic, based on our data, itsgoal was primarily to see if multiple strategies on household size would be typical or if a modestly complex system would devolve to only one viable strategy. As the graphics show, many runs of the model converged from random starts for the agents in similar times to a multi-strategy result.

_

A transformational growth matrix A transformational growth matrix

n Key: EconG - Economic Growth, EnvH - Environmental Health, AdlSoc - adolescent socialization, Educ - Education Health - human health and medical infrastructure StndL - standard of living SocInf Social infrastructure DemG democratic governance PopP population.

n A TGM is a matrix of qualitative measures of two-way influence between indexical variables. The values are to be estimated and normalized to between -1 and +1. using quantitative and qualitative techniques.

Transformational Growth TheoryTransformational Growth Theoryn Edward Nell, A general theory of transformational growth (1998) dispensed

with equilibrium theory and argued that the capitalist economy, even more so than earlier economies, was in a continuous process of transformation: a Hereclitan view that you cannot step into the same version of capitalism two years in a row.

n Among his many arguments he proposed that under capitalism prices were shaped by competition and so were salaries, inputs, labor organization, business plans and so forth but there were no equilibriums to be had and transformations in efficiency as well as power shape the economy.

n Thus, to understand what was happening in a given place and time a TGM would prove indispensable.

n The economy is shaped by the distribution of power in and out of the market, in the financial sector as well as in the productive sector. Obviously, this distribution produces differential constraints on behavior over time and place.

n When distortions in the financial sector lead it to stop tracking the productive sector well problems can ensue: the greater degree of freedom in the financial sector put the onus on it to conform rather than the other way around.

Modeling Benefits of a TGMModeling Benefits of a TGMn By incorporating a variety of focus group, survey and network

analysis data into a normalized matrix such as a TGM we can make comparisons between different sectors of an urban area much more meaningful.

n A number of TGMs can capture the interactive character of the infrastructure and its differential effective character across the urban space.

n A TGM can amalgamate qualitative and quantitative data easily.

n Development traps operating in one area but not in another may show up via the multiple TGMs.

n The TGM can serve as a focal point for models: we are developing an Agent Based Model (ABM) that will allow both agent behavior to transform the local TGM and allow policy inputs to modify it as well even as the TGM serves as a basic descriptor for current returns to agents’ strategic behavior.

Social science concernsSocial science concernsn The ecological notion of constraint translates for social scientists into a

concern with power:

n People in human societies are not equally empowered.

n Rights are different than empowerment.

n The distribution of goods may be unfair (constrained) but this can be readily modified through legislation, popular pressure (votes), rebellion or even revolution often without major economic impacts.

n Markets votes are weighted by income, and usually the rich consume and pollute more than the poor and governments and tax codes pander to the rich but none of this qualifies as either necessary nor is the basic hierarchy fundamental as the range in national measures of inequality attest.

n None of these, or any other concerns about power or distribution have the same ontological existence as basic ecosystem constraints (they are a social problem not a physical one) even if in terms of information theory they may appear similar -- and at some point in modeling this must be clear.

An urban political ecology model to study structural adjustment and

other policies An urban political ecology model to

study structural adjustment and other policies

n Collaborate with a local institution (e.g. Ardhi University in Dar es Salaam) situated in a large urban area in a country subject to external advice.

n Select 200 sample points using urban classification based on diachronic (25 years) remote sensing imagery and local expert classification.

n Use a variety of data (focus group, survey, secondary, institutional network analysis) to construct TGMs (one for each major category of profession in each locale).

n Create an ABM in Simphony that can capture a variety of household growth paths for each of a number of professions, have its agents with locally realistic distributions of assets and behave as semi-realistic agents within a GIS environment shaped primarily by the TGM for their profession in their locale.

n Assess past and current policies with impacts in the urban area.

A Proposed ABM for Dar es SalaamA Proposed ABM for Dar es Salaamn A TGM for each local profession (grouped into a small set of

comparable professions) will be constructed for each urban class using data from the sample points in the class areas.

n Agents will begin with a locally representative range of values for each indexed variable in the TGMs and will pursue dynamic strategies at one time scale, then emulate better performers within their profession or members of institutions (churches, NGOs, mosques) with which they have ties.

n Different indexical variables will evolve at different time scales: the values in the TGM will be updated at different time scales based on the current state of agents’ values and the agents will be allowed to emulate (using a stepping rule) the values of better performers at different time scales for different indexical variables.

n Highly educated or connected households (agents) will also be able to take advantage of an enhanced range of emulation possibilities based on our analysis of historical and current trends in the data across the urban space. These trends will show up in the model as an prototype agent that can be emulated.

The Dar es Salaam ABM(continued)

The Dar es Salaam ABM(continued)

n The ABM will allow us to externally modify the values of the TGMs in order to emulate the impacts of structural adjustment policies (e.g. increased class size due to cuts in education might decrease the value of education or cuts in health care might increase disease and decrease earning capacity etc.).

n Changes in the communication levels between institutions (NGOs and State institutions) might enhance services and this can also be incorporated into transforms in the TGMs.

n Increases in AID within specific sectors and implemented in particular parts of the city can also be implemented in the model.

n Epidemiological models can also be used to modify the TGMs in different parts of the city or for different (in terms of risk behavior) professions.

n Since the model can use both a spatially distributed set of TGMs, in the form of a GIS that agents access, and can directly draw from statistical data (using R) we have many ways of constraining behavior in the ABM with real world data and we will use this capacity to constrain agents to explore realistic options.

ReflectionsReflectionsn The urban political ecology model differs in a number of fundamental

ways from any model derived in a straight-forward way from plant ecology (such as panarchy):

n Causality is more flexible in terms of scale (both spatial and time).

n There is room for a fairly realistic incorporation of power (the social version of constraint) and its rapid modification (rather than relying on laborious stylized and crudely interconnected transformations).

n It fits much better a seriously nonergotic world because it has much greater openness to rapid change.

n Without precluding the possibility of some emergent properties being explained in reductionist terms, it incorporates both internal (model) complexity and on-going inputs from real world (people generated) complexity.

n It encourages modelers to explore the ambiguity of resilience: for whom and by what means or at what social cost or with what social benefit.