Mountains and Landscapes as Heuristics

In the 1930s, evolutionary geneticist Sewall Wright pulled together research strands in the biology of inbreeding, the genetics of coat color in guinea pigs, statistical methods (including path analysis), and mathematics that codified the changes in gene frequencies in populations as a result of natural selection, mutation, and migration.

His resulting description of these threads set the stage for qualitatively different perspective on the evolutionary process.  Wright described his perspective as a “shifting balance” model of evolutionary change, and it highlighted the role of small populations in the transitions between periods of high and low fitness.  This pattern, which followed from his use of the term “drift”,  describes the fluctuations of gene frequencies that result from the random sampling of small populations.  This random sampling comes from mating in small populations that, because of chance, produces small deviations from the numbers of genes originally represented in the population.

Wright’s Shifting Balance perspective coincided with his introduction of the adaptive landscape as a term to describe the space in which random fluctuations of gene frequencies in small populations could push the populations away from adaptive peaks or periods in which they were reproductively successful, and which would in turn allow natural selection to push them towards new adaptive peaks – areas of differential reproductive success.

Though Wright’s perspective on evolution is controversial (in a generative way), the perspectives and tools that emerged from his ideas have endured.  For example, Wright’s work preceded algorithmic approaches to optimization problems in mathematics, networks (traveling salesman), metallurgy (simulated annealing), and artificial intelligence – to name a few

The process of Shifting Balance is described as a series of three dynamic phases:

Phase 1, the exploratory phase, the action of small groups explores new combinations. Most stay on the suboptimal fitness peak (reasonably successful), but some get caught in adaptive valleys (unsuccessful).

In Phase 2, selection causes the groups that are in the adaptive valleys to move toward new, higher-fitness peaks.

Finally, in phase 3, groups at higher fitness peaks send off migrants helping other groups move to higher fitness peaks.

Phase 1: The Exploratory Phase

Phase 2: The Selection Phase

Phase 3: The Migration Phase

While Wright’s process was intended for population genetic systems, an increasing convergence between social processes, cognitive psychology, technology, ecology, and creative practice suggests that the concepts apply well to the exploratory, form-finding processes that precede the design and production of materials and services. The implementation of the Shifting Balance process as a analog for social and creative strategy is useful for the production of highly original and robust creative solutions – or, at least it’s a testable hypothesis.

For some, analogies between biological and social processes are difficult to comprehend.  However, the design of services and interactions is dependent on the ordering and reordering of processes, materials, people, and ideas. Combinations and recombinations of these things, when developed thoroughly and communicated, can impact the delivery and relational aspects of individuals working in cooperation or separately.

We could envision this process as a sort of charette (period of intense design in collaborative groups) activity where:

1. The exploratory phase initiates adaptive schema (creative combinations) which are driven by the interactions, specializations, and diverse perspectives of small groups;
2. Intergroup selection resulting from evaluation, the inherent heterogeneity among groups, and intended service platforms begins the iterative process of amplification of good combinations;
3. Export and translation of valuable forms/schema to other groups in order to test them against different problems, social contexts for cooperation, and consumptive patterns.

The immediate benefit of this strategy is the demonstration of expertise in practice, the role of discourse, and the chance events that can drive innovation.   Participants from different disciplines will have to opportunity to observe and engage in creative problem solving within highly diverse communities. Here the focus is on collaborative ideation followed by problem-solving across disciplinary and expertise-based boundaries and ultimately an exercise in cooperative translation, storytelling, and communication.

There is enough social scientific research to at least point to the benefit of diverse groups, although it would be worthwhile to have a better handle on an ideal number – i.e. what counts as a small population.  Plus, how do we go about choosing?  What is the process of selection…or should we instead be saying, “What is the process of attachment?”  And finally, are there specific patterns of translation or dissemination that we should aim for?  For if migrants endowed with the most successful schema do disperse and link up with others, they have an opportunity to cooperate and raise the capacity the other groups elsewhere. But through which mechanisms to we initiate and implement these processes?

There are a few other ideas that seem uniquely coupled to the Phases of Shifting Balance. An example is the goal of participation as a unique form of empowerment in community planning exercises. One particular model of participatory engagement provided by Conde et al. (2004) is used in the context of climate change planning (below).

The Landscape of Participation

This example shows transitional categories in participation. When viewed through a model of culture which emphasizes process over characteristics, these are skills acquisition categories that indicate differences with an impact on fitness – i.e. reproductive success.

Each category represents a different level of engagement, a level that itself suggests a tighter relationship between participants and the tools of participation or cooperation.

1. Informative participation is an exchange of information, which may or may not be meaningful.
2. Consultation requires that participants begin asking questions as well as providing information.
3. Functional engagement means that different participants identify and agree to share goals, thus ordering their actions in accordance with each other.
4. Interaction means the initiation of feedback, where signals and shifts in the participation is met with responsiveness and dialog with the others.
5. Self-motivated participation is demonstrated by the points at which processes are acquired and reorganized by the participants themselves.
6. Migration ultimately expands the instances of participation which have been successful, sharing them with other communities, and finding cooperative allies elsewhere.

References:

Conde, C., Lonsdale, K., Nyong, A., & Aguilar, I. (2004). Engaging stakeholders in the adaptation process. Adaptation policy frameworks for climate change: Developing strategies, policies and measures, 47–66.

Johnson, N. (2008) Sewall Wright and the development of shifting balance theory. Nature Education 1(1)

Wright, S. (1977) Evolution and the Genetics of Populations. Vol. 3: Experimental Results and Evolutionary Deductions. University of Chicago Press, Chicago.

Adaptation, coping, resilience, and robustness have similar definitions, but they sometimes have different technical definitions across disciplines. Their different meanings contribute to their value, and they highlight the differences in perspectives that each scientific community contributes.  However, the details matter for distinguishing important components of systems and what aspects might be suggestive for new insights or that might be responsive to intervention or assessment.  It’s also important to establish common ground meanings when communities get together and work towards common goals.

There is a benchmark article Resilience, Adaptability and Transformability in Social–Ecological Systems that does a much better job at pulling together the literature than I do here, and I came across it after writing much of what is in this article.  It is also the narrative used by the Resilience Alliance for their activities.

The following represents some of my notes and thinking as I try to sort out the definitions on my own.  For me, it means asking how different perspectives contribute to the ways in which we interact in socio-ecological systems.

Adaptation
The Intergovernmental Panel on Climate Change (IPCC) 4th Assessment Report defines adaptation as:

Initiatives and measures to reduce the vulnerability of natural and human systems against actual or expected climate change effects. Various types of adaptation exist, e.g. anticipatory and reactive, private and public, and autonomous and planned. Examples are raising river or coastal dikes, the substitution of more temperature-shock resistant plants for sensitive ones, etc.

This definition takes its function from the ability of humans to manipulate their environment, making it better suited to human-identified goals and interests, even if acting on behalf of other organisms.  Some synonyms include alteration, modification, redesign, remodeling, revamping, reworking, reconstruction, conversion, adjustment, acclimatization, acclimation, accommodations, habituation, acculturation, assimilation, and integration.

Adaptation is also used to describe genetically-accumulated evolutionary change over time in organisms as a response to natural selection. This is different from the case where manipulating the environment substitutes in the short-term replaces the pressure of genetic adaptation over the long term.

So I suppose this is why it calls to mind a version of evolution based on characters acquired in its lifetime (commonly known as Lamarckian inheritance)–if only for the appropriation of the term adaptation to refer to intra (within) generational processes and not inter (between) generational processes.

Adaptation for evolutionary biologists typically means processes through which a population becomes better suited to its environment over the course of many generations, often through natural selection.  A great deal of debate and research has been directed at how we recognize adaptation in hindsight.  This is because it can be difficult to state the causes for the evolution of a trait when we do not have direct observation and only historical signatures to learn from.  Most notably this was discussed in “The Spandrels of San Marco”, a paper by Stephen Gould and Richard Lewontin (1979) that uses an analogy from architecture for the evolution of organismal form and function.

I agree that changing the environment in the ways mentioned in the IPCC definition will likely limit vulnerabilities for humans and other populations.  However, there is an implicit assumption here that the goal should be for humans NOT to have to adapt over a course of generations–despite the inevitability of genetic change over time.  It presupposes an assumption of stasis – and a very western one when compared to eastern notions of change and mutability.  Richard Nisbett catalogues how some of these assumptions about change and stasis in his book The Geography of Thought.  For me, it depends on what time scale one is looking to understand if stasis or change is more relevant.  Still, I think its difficult to argue anymore that stasis is more relevant than change.

The necessary question should not be IF we should adapt (genetically or by manipulating the environment). Instead we should ask, “What are we adapting to and how are we getting there?”  Will humans and other populations be adapting to artificially-supported ‘vulnerability balloons’ as we are almost surely doing now through our uses of technology and fossil fuels?

This question of adaptive goal is important because the IPCC definitions include definitions of costs and benefits with its description of adaptation.  To what goal are these costs and benefits applied?  Within the frame of a generation or an organism’s lifetime, explicating goals may make sense, but ascribing goals to a ecosystem – much less whole populations – gets very very slippery.  You start to need some way to implicate who or what is writing that mission statement.

Similarly the IPCC includes adaptive capacity in its glossary as the ability, institutions, and resources that can be used to implement adaptation measures.

I think this is all a bit confusing, and I feel it makes more sense to reserve the definition of adaptation for genetic, phenotypic, and behavioral attenuation of organisms or systems to their environment across generations.  To describe the processes that organisms and systems use during their lifetimes I think we need a term that encompasses more variability, one that is less blatantly anthropocentric and functionalist in its approach to socio-ecological coevolution.  We also need a long view on systems not ones that are limited to single generations only – something that the biological definition of adaptation retains but that the socio-technical one does not.

Borrowing from the literature of evolutionary biology, behavior, and developmental biology, plasticity seems far better suited to the processes of environmental manipulation being described by the IPCC.  This is because it references a material (plastic) that maintains its basic molecular structure while having variable capacity to take on any number of manipulations or forms.

Coping and Plasticity
The terms coping and adaptation are sometimes used interchangeably leading to confusion.  Here I think there is some opportunity to disentangle the two.  A compilation of brainstorming sessions by groups of development practitioners in Ghana, Niger and Nepal described some differences which were then documented in the Climate Vulnerability and Capacity Analysis Handbook.  The results of the group’s sessions were pointing to what I think was a difference between 1) consistent and conscious actions to reduce vulnerability (adaptation) versus 2) ad hoc solutions (coping).

It’s worthwhile to differentiate coping and adaptation as within and between generation processes, respectively.  Biologists use plasticity to describe the ability of an organism or group to adjust within its lifetime via behavioral or developmental responses to the environment.  This may indeed include manipulation of the environment to decrease vulnerability.  Phenotypic plasticity is a description that could easily encompass artifacts, behaviors, institutions, and aggregations of resources as extensions of an organism’s phenotype.  It invokes important concepts from evolutionary biology including the role of cooperation in building and maintaining extended phenotypes (such as aggregations of useful materials like insurance, band-aids, and water) or how phenotypic reaction norms can change in response to different environments–shedding light on why a strategy in one environment may not be as successful in another.  There is further correspondence here with plasticity and the concept of developmental canalization (that organismal systems can get locked in to specific trajectories) and with the concept of path dependence in the development of economic and institutional systems.

So a better definition of plasticity might re-appropriate the IPCC’s definition of adaptation and rework it as:

An adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities. Plasticity operates through cognitive (sensing), social (interactional), physiological, and other mechanisms that can adjust to a wide range of variability. Plasticity is the ability to respond to variability and a range of realized and possible futures continuously and in a sustained approach. Plasticity or coping strategies attenuate the use of resources to local needs and involve planning that hybridizes old and new knowledge and strategies in an exploratory process.

Here I think this definition makes it much easier to bridge what may be happening at a physiological level (cellular temperature variation, sweating) with responses at an artifact level (clothing, ventilation) and an institutional (e.g. policies towards what it means to be cool).

This is because the term plasticity explicitly invokes a connotation of variability, while adaptation feels more like a description of how well two things (in this case organism or population and environment) fit together.  Clearly, if the environment is highly variable we need variability in our systems, not assumptions and values of how well we already fit and work within it.

Coping, on the other hand, seems pretty straightforward.  Survive.  It makes sense to leave a lot of variability open for this one, because when it comes time for coping strategies, any and all tactics may be appropriate.  But then again, there can be ways to cope that are more responsive than others.  But I think this starts to dig into a definition of resilience or robustness, where the system properties begin to matter more than than how they manifest themselves in practice.  What I mean by this is that as people, organisms, and ecosystems attempt to cope with change, their ability to draw on networks or strategies for coping is itself embedded in the system.  Some systems, as a function of their structure, cope better than others.  Consequently the adapt better than other too.

Resilience
The Climate Vulnerability and Capacity Analysis Handbook adapts its definition from UNISDR (2009) defining resilience as “the ability of a system to resist, absorb, and recover from the effects of hazards in a timely and efficient manner, preserving or restoring its essential basic structures, functions, and identity.”

The IPCC defines resilience as “the ability of a social or ecological system to absorb disturbances while retaining the same basic structure and ways of functioning, the capacity for self-organisation, and the capacity to adapt to stress and change.”

While Walker et al (2004) define resilience as “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks.”

In these cases resilience emphasizes a system’s ability to maintain or return to specific structural or functional features–i.e. to maintain its identity, its durability, its persistence.  But as noted by Erica Jen in her article “Stable or Robust? What’s the Difference?” (2005), the choices of features or structural elements that we attend to are important for assessing both the capacity and quality of that responsiveness to change.

So what is the function, what is functional, and for whom?  Definitions matter.

One way to think about resilience is to imagine a couple of different water balloons.  One balloon is filled halfway full.  Another is filled so that the latex rubber that composes its surface and membrane is stretched tightly to hold the water in.  Now you can throw both balloons back and forth between each other, and neither may pop.  But what do you think will happen when the balloons are stretched, twisted, or allowed to drop on the ground where a twig might be a hazard to the already tense surface of the overfilled balloon?  It will probably pop and spill the water out.

A system’s resilience is a lot like a water balloon, and the degree of resilience is determined by how much water is forced into the balloon, the size of the balloon, and how much it is pushed to its limits.  We might think of the balloons shape, its ‘throwability’ or the thickness of its membrane as examples of functional or structural elements.  In most cases, we are looking at how well the balloon is able to maintain it shape and its continuity despite being stressed – i.e. it is functionally a ‘water balloon’, it has a round shape, and responds to the exterior and interior pressures of air and water.

Rarely do we think that a water balloon might reconfigure itself, rearranging the organization of its functions, structural elements, or features to be able to accomplish the same task differently.  What would happen if the water and the balloon separated or if the water balloon system was able to draw on other systems (e.g. refrigeration) to change the relationships between its functional elements?  What if we no longer simply considered only the water inside of the balloon as the system responding to the task of throwing? What if the throwing and catching movements were also included?  Would we still think of a resilient system, or would we start to walk a path of robustness–of being able to adjust the definitions and constraints of the systems themselves in pursuit of coevolutionary relationships between them?

Robustness
Robustness is a different beast altogether – literally.  While resilience is focused on maintaining a system, we can describe robustness as the ability of a system to change and in doing so to respond to environment and to develop entirely new functions as a result.

Some argue that robustness describes the ability of a system to withstand mutations and maintain its phenotype or “shape” as a result (Wagner, 2005).  Instead I think there is a greater correspondence of robustness with transformation as used by Walker et al (2004).  Transformability is “the capacity to create a fundamentally new system when ecological, economic, or social (including political) conditions make the existing system untenable.”   I’m less sure about the “untenable” part of Walker et al’s definition.

Robustness is the ability of a system to evolve system functions, not simply maintain those that already exist.  In this way, an analogy can be drawn between adaptation/robustness and plasticity/resilience.  Similarly, I think robustness has a quality of being parametric.  Parametric architecture has the quality of being built from common construction principles, but by varying the parameter values of those rules of construction, endless forms become possible.

References

Walker, B., C. S. Holling, S. R. Carpenter, and A. Kinzig. 2004. Resilience, adaptability and transformability in social–ecological systems. Ecology and Society 9(2): 5. [online] URL: http://www.ecologyandsociety.org/vol9/iss2/art5

UNISDR, 2009. Terminology: Basic terms of disaster risk reduction and IISD et al, 2007. Community-based Risk Screening – Adaptation and Livelihoods (CRiSTAL) User’s Manual, Version 3.0.

Climate Vulnerability and Capacity Analysis Handbook

http://www.careclimatechange.org/index.php?option=com_content&view=article&id=25&Itemid=30

IPCC, 2007: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Annex I., M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 976pp.

Stephen Jay Gould and Richard C. Lewontin. “The Spandrels of San Marco and the Panglossian Paradigm: A Critique of the Adaptationist Programme” Proc. Roy. Soc. London B 205 (1979) pp. 581-598

Wagner, Andreas. 2005. Robustness and Evolvability in Living Systems (Princeton Studies in Complexity). Princeton University Press.

riskTable

Key requirements for recognizing trends in disasters include being able to:

1. differentiate between high frequency trends and low frequency trends (partly because cognitive biases inhibit objective estimation),
2. the potential for changes in their relative frequencies and path dependency (low frequency becoming high and vice versa),
3. the cumulative impacts at different temporal and spatial scales of interaction, and
4. the emergence of threshold effects where small impacts can have big effects.

The rise in frequency of natural disasters is being compounded by population growth (especially in urban, coastal, and low-lying areas) and increased vulnerability because of interactions among resources and risks (see table 1 for examples). Many natural phenomena tend to be recurrent. For example, diseases re-emergence in and out or areas and population, sometimes in cycles, while often borne from social-ecological network differentiation (Janssen et al., 2006). These recurrences can affect the same regions and populations again and again–either out of geographic, genetic, or behavioral specificity. Impacted populations have narrow opportunities (if at all) to restore livelihoods and coping mechanisms between events. This can accelerate chronic vulnerability.

Key trends discussed and communicated in the literature relate sea levels, temperature, precipitation, resilience, and extreme events to climate change (Prasad et al, 2009). While these are specifically the result of abiotic processes, other, underemphasized, social trends emerge that are important for managing coping strategies–especially where cities are concerned. These trends include:

Cultural Preferences: This is perhaps the least understood of any emerging trend, and we don’t know much about how the various components of this trend are distributed at any given moment. Cultural preferences includes things like how new skills, uses, and behaviors are acquired, the ways they are arranged in everyday life to fill particular needs, how existing artifacts or concepts are appropriated, and what it takes for small, limited sets of practices to widen and become normalized in larger populations. As a trend, many human systems are moving towards knowledge networking which will accelerate normalization. Less frequent are the hybrid ways of creating new coping strategies that build on other unrelated themes or needs. As a result it is pretty easy for most disaster management and preparedness disciplines to dismiss it as a leading component of interest.

Uncertainty and Risk Diversification: As the intensity of experience and practices with technologies, the environment, and human population increases, uncertainty and the recognition of risk becomes more evident. This is to say that we tend to project more uncertainty and develop a larger number of risks as our knowledge of the environment widens. Thus, while there are real and significant increases in the number of risks, the increase and perceived impact is also a function of our own cultural sources of knowledge production and risk assessment. This in no way delegitimizes the risk of climate driven disaster. It only adds a unique dimension to our reception and relationship with them.

Urbanization: In 2008, the global population became equally distributed between rural settlements and cities. This trend will continue for a variety of reasons including individuals’ search for economic agency in cities. It highlights a broader pattern of preferential attachment–a social phenomenon in which people (agents) tend to want to join up with other agents that have multiple connections, either to other people, things, or places. It also signals a significant perceptual shift in our understanding of ecology and its anthropogenic impacts–away from systems where humans are seen externally to one in which the landscape is unequivocally ‘disturbed’ and redistributed (Ellis and Ramankutty, 2008).

Ecosystem Service Disruption: Healthy ecosystems are a keystone of resilience. They buffer vulnerable populations from the impacts of disasters by maintaining critical life support services such as soil for agriculture, water filtration and sequestration, nutrient cycling, organic waste recycling, gas exchange + air pollution mitigation, and the ambient commons (McCullough, in prep) which support the awareness of a continuum between culture and infrastructure.

ad hoc Solutioning: In India, the Hindi term Jugaad describes technologies that are patchworks of on-hand materials to fix and make due with what is convenient and ‘affordable’. They build (no pun intended) on an ease of use and innovative skill in the context of personal or collective economic agency. They can insert sustainability using biodegradable, local, and available materials–deemphasizing systems of manufacturing while emphasizing individualism and craft. However, jugaad may also substitute expectations for semantics, trading durability for extended (or distended) service relationships in the absence of independently verifiable standards. The impact of this behavioral tactic with artifacts is that technologies can have a low threshold for failure because they depend on service and labor for continued maintenance. When the services become otherwise compromised, the artifacts create further risks.

Occupation of High Disturbance and/or Diversity Landscapes: Along with trends in urbanization and ecosystem services, people tend to locate in regions where resources are abundant and that tend to support a large amount of diversity. One of the main ecological predictors of biological diversity is the ongoing process of disturbance, which continuously opens up new niches and creates genetic diversity across populations. This points to the presence of large urban settlements in areas prone to disturbance and potential disasters either from earthquakes, flooding, cyclone, tsunami, or wildfire, for example.

Now what do these trends mean for emerging health risks in the context of climate change?

References:
Ellis, E. C., & Ramankutty, N. (2008). Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment, 6(8), 439–447.

Janssen, M. A., Ö. Bodin, J. M. Anderies, T. Elmqvist, H. Ernstson, R. R. J. McAllister, P. Olsson, and P. Ryan. 2006. Toward a network perspective on the resilience of social-ecological systems. Ecology and Society 11(1): 15. [online] URL: http://www.ecologyandsociety.org/vol11/iss1/art15/

McCullough, M. in prep. Ambient Commons. http://www-personal.umich.edu/~mmmc

Prasad, N., F. Ranghieri, F. Shah, Z. Trohanis, E. Kessler, and R. Sinha. 2009. Climate resilient cities : a primer on reducing vulnerabilities to disasters. Washington (DC) : World Bank Group Info Shop. ISBN 978-0-8213-7766-6

Daniela Plewe’s discussion brings me back to some thoughts and notes I made about Marcel Duchamp’s Coefficient d’Art. Duchamp described it as:

“An arithmetical relation between the unexpressed but intended and the unintentionally expressed.”

It is intended to describe the difference between what artists intend and what the spectator perceives.  For Duchamp, this difference is in the act of communication or transaction, where certain differences and attributions of value are made out of the interaction among individuals.  It this coefficient that structures the viewers engagement with artifacts and allows them opportunities to appropriate objects to their own needs and ends.

For Duchamp, the coefficient of art could be good (+), bad (-) or indifferent (=), but the sign of the coefficient had no bearing on the effectiveness of the work itself–only the difference between the agency of the artists to produce a desired effect in the minds of the spectators.  The effect itself is up for further negotiation between them.

Mutual information is a similar concept to the coefficient of art, but it comes from information theory and describes the amount of information one thing tells about another thing. In other words, it is the reduction in uncertainty of one thing due to knowledge of another. If we ask how information (and consequently, meaning) is shared between different sources of uncertainty (like an object and a spectator or an object and its artist), we may be able to get a sense of how they are connected and how they might respond to each other.

Mutual information is helpful as a concept because we want to understand how interactions vary with one another–i.e. how interaction values may/may not change as a result of signals, actions, and assumptions.

A component of mutual information is information entropy. Entropy is a measure of uncertainty associated with a variable and quantifies the information contained in a message.  It is similar to the coefficient of art; it may describe the uncertainty associated with an artwork as judged by the spectator.  Conversely, it could describe the absence of meaning when one does not know the value of the work.  Likewise the spectator may themselves exhibit high entropy (high uncertainty) relative to the artist if the artist knows little about the spectator and how they will perceive the artwork….at least that’s how I think it would go.

The coefficient of art is a compelling concept.  It suggests that that art has an effect, and if an effect–value in context.  Describing that value is very close to the describing what difference the work of art makes, either to the spectator or some chain extending through them.

Borrowing from evolutionary and network theory, one could pull in a set of relationships between interacting agents that describe how networks evolve and persist. Relationships endure over time from the benefits of interaction. In network reciprocity, entities pay a cost, c, while their number of neighbors, k, receive a benefit, b. If b/c > k, where the ratio of benefits to costs is greater than the sum of neighbors, the network persists because its members are gaining as a result of their interactions.

Duchamp’s coefficient of art (hereafter described using the greek letter psi, ϕ; see also: epistasis), approximates the number of neighbors, but as indicated by it separation from the actual effect of the work itself, says nothing about costs and benefits. ϕ approximates k, or rather the reciprocal of k, because as the number of neighbors (or spectators of the work) increases, the likely ability of the artwork to communicate intent, decreases. This is because of variation among the spectators who may either not be well-understood by the artist or who are perceiving differently or because the artist. Interestingly, ϕ always assumes artistic intent. If ϕ is low, it may be the ‘fault’ of the spectator, the inability of the artist to realize that intent, or of some other intervening factor.

But what about art that is created beyond intent such as generative, algorithmic, or emergent artworks?

ϕ may also be a bound on the ability of artifacts to bridge social groups, as in the case of boundary objects that have multiple uses. The intent of the maker of that object is only partially achieved, but may clearly be appropriated to serve other purposes. Here we might similarly invoke a coefficient of use–or a measure of intent in use that transforms the intent of the artist.

Far from achieving certainty, at least the idea of ϕ, of a coefficient of art, starts to unlock more questions about translation and meaning between objects and people–and of the directionality of interactions between people.

Where People Live

Anthropogenic Biomes as a Region for Research in Evolutionary Design Ecology
Many systems of classification for regions ignore the integration of human influence and ecosystem form, process, and diversity. This situation was common when I was in school and we learned about different ecological regions that were described largely by vegetation type and the weather patterns.  A definition of region that is based on many interactions between society and nature, including perspectives  on global patterns of sustained direct human interaction with ecosystems, may be appropriate for weighing studies of human health, its interactions, and driving factors. Anthropogenic biome describes a recent and perhaps better system of regional classification than have previous definitions (Ellis and Ramankutty, 2008) which have tended towards pure forms of nature or the separation of nature and society.

Anthropogenic Biomes: Definition
Anthropogenic biomes are similar to ecological biomes: they describe patterns of vegetation, climate, and ecosystem processes. However, they also take into account the anthropogenic influences of land use and population density on ecosystem processes. Ellis and Ramankutty characterize anthropogenic biomes as heterogeneous landscape mosaics, combining a variety of different land uses and land covers. Some of this heterogeneity is driven by natural landscape variation, as well as human enhancement of natural landscape (e.g. intensive agriculture) and human created landscape (e.g. construction of settlements and transportation systems).

The Regional Classification System they developed is as Follows (Ellis and Ramankutty, 2008):
Dense Settlements: Urban, Dense Settlements

Villages: Rice Villages, Irrigated Villages, Cropped and Pastoral Villages, Rainfed Villages, Rainfed Mosaic Villages

Croplands: Irrigated Cropland, Residential Rainfed Mosaic, Populated Irrigated Cropland, Populated Rainfed Cropland, Remote Cropland

Rangelands: Rangelands, Populated Rangeland, Remote Rangeland

Forested: Populated Forests, Remote Forests

Wildlands: Wild Forest, Sparse Forest, Barren

Of Earth’s 6.4 billion human inhabitants:
40% live in dense settlements biomes (82% urban population),
40% live in village biomes (38% urban),
15% live in cropland biomes (7% urban), and
5% live in rangeland biomes (5% urban)
0.6% live in forested biomes.

Asia and Oceania have the most diversity in the distribution of these regions around the world.

Global Anthropogenic Biomes

Further refinement is possible (Alessa and Chapin, 2008) by resolving distributions of social values, dietary patterns, movement patterns, resource use and between local and regional scales, inter alia.

Why Anthropogenic Biomes Matter for Public Health and Other Forms of Research
Anthropogenic biomes are a more accurate description of broad ecological patterns than are systems that exclusively describe vegetation patterns based on variations in climate and geology. Likewise, anthropogenic biomes may be better at representing patterns of human interactions with the environment and describing the driving factors in health outcomes. There are multiple reasons for this that stem from the varied roles that ecosystem, climate, cultural, and social relationships enact in dialogue with each other.

Anthropogenic biomes differ substantially in terms of basic ecosystem processes (eg carbon emissions, reactive nitrogen) and ecosystem biodiversity. These factors in turn affect the relative availability of resources for that region, including and especially ecosystem services like clean air and water and nutrient availability for agriculture.  Furthermore, they must necessarily feed back into human ways of knowing and interacting with the environment.

Anthropogenic biomes can be connected to global patterns of ecosystem processes, along with anticipated future increases in human influence on ecosystems and the associated health outcomes due to climate change-driven risk factors.

Genome by environment interactions may be particularly relevant at this scale of interaction. The region definition is appropriate to human movement patterns and thus exposure to sources of chronic and acute risk from disease and consumption patterns.

The land use type itself determines a wide variety of factors including interactions with other humans, livestock, dietary consumption, levels of hydration, energy intensity, and other factors.

Culture, ethnicity, and language are also important in response to land use and domestic patterns of consumption ranging from food use and taboos, communication of lifestyle and health options, provisioning of nutrition, water, and energy, availability, and the use of technology to process and maintain different lifestyle patterns.

In each of these regional definitions, the interactions between landscape and human activity affects affluence, access to health care, and political regulation which suggests that these are are other possible subdivisions since these regions correspond to human social, transport, technological, and social networks–especially in dense settlements versus villages and remote areas.

For these reasons, anthropogenic biomes may provide more of a mosaic-like image from which to base categorizations used by clinical and other studies of health compared to political and continental boundaries which conventionalize migration barriers and tribal relationships. Geographic and political definitions will slowly shift, leaving only historical genetic signatures. Furthermore, anthro biomes are not specific to any particular disease or health outcome. They may encompass suites of infection and disease patterning where behavior, exposure, risk, and land use are correlated. They may also be indicative of linked health outcomes at the physiological level where, for example, musculoskeletal disorders and endocrine system perturbations are bound by human-influenced ecosystem interactions.  Or they may suggest psychological correlates, linking cognition and landscape to disease and health risks.

The main point to consider is that ecological relationships, including land use and human infrastructure development, script behavior and consumption in ways that drive health outcomes. Understanding human influenced ecosystem patterns helps us identify areas of positive feedback between health risks, land use, population density, and the construction of everyday life.

References
Alessa, L., & Chapin, F. S. (2008). Anthropogenic biomes: a key contribution to earth-system science. Trends in Ecology & Evolution, 23(10), 529–531.

Ellis, E. C., & Ramankutty, N. (2008). Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment, 6(8), 439–447.

It amazes me that in all of the discussion documented in the article, there is never a mention of designers, artists, or any other such expertise that actually spends the majority of its effort on communication, messaging, experience design, and the use of sensory mechanisms to motivate behavior. It makes me sad that there is the recognition that, when it comes to communication, it’s always about the researchers doing the communication. This can be improved, yes, and there are also many design-thinking guidelines one can pull out of the article. How many can you spot?

The Green Issue – Why Isn’t the Brain Green? – NYTimes.com.

scanner ants

The CEMA homepage is showing an image of scanner that has opportunistically been colonized by ants (anyone know which species?). I was present at the offending attack, and I have this to say. I didn’t see it so much as an attack as it was (more perversely) an underanticipated observation that ants had quietly moved into an (apparently) unused and undisturbed piece of late 20th century technology- that of the document scanner.

While this may have been felt by some as an attack on our morals of human-hood and right-living (ants and scanners shouldn’t mix, right…er…right?), to me this was much more the most delicate and profound expression not of nature but of the social world in which we live. The most amazing thing to me is that a colony of ants could have arrived and decided that a scanner would make a good home. Perhaps there were some legacy muffins adding allure to the crystal glass and step-motor, but maybe the ants were looking for something held up in the ambient waves of electrical heat left over from un-nourished scans of students’ faces, buttocks, book chapters, and collages.

No..I think this is exactly where we want to be…where mixes and happenstances converge out of nothing more than the desire to find place, continence in the “other”, and the cheap thrill of being where you aren’t supposed to.

On checking up on their status, they are gone from the scanner…pupae and all. I’m not sure if they left on their own accord or if they were kicked out. Where did they go? The water cooler perhaps? As for next time, I’m keeping my fingers crossed that discovery doesn’t correlate with disentanglement. I’d like to keep my scanner ants…who knows…they may have figured out something that we haven’t.

National Intelligence Assessment on Climate Change (PDF)

The compelling section of the report was its recognition of its own limitations, and the kinds of tactics that the intelligence community needs to better understand complexity and difficult social, economic, and environmental issues.

Our analysis could be greatly improved if we had a much better understanding and explanation of past and current human behavior. Continued research to model social human dynamics at the individual and society level would support this improved understanding. This would necessitate the ability to integrate social, economic (infrastructure, agriculture, and manufacturing), military, and political models. Continued research in these efforts—while a significant challenge—could have high analytical payoff. In the interim, assessing the future of a society’s evolution will by necessity be a scenario-driven exercise and an imprecise science. The continued use of outside experts is critical to our success.

It’s somewhat comforting to know that at least the intelligence community is starting to learn that it takes diverse groups of people and disciplinary perspectives to solve difficult problems. Who knows, maybe they will even be willing to seek out non-traditional perspectives from the arts and/or oppositional discourses in their futurecasting.

location tracking phone

Biologists have been performing similar studies on animals for years, using radio tracking devices and similar forms of locations awareness. However, because people tend to be difficult to keep track of, subject to influence from experimental methods, and resistant to monitoring by others, it has been previously difficult to get this kind of accurate data about humans.

Without recapping the study itself (you can read the original abstract and related news stories from the links below), there are many reasons why these data are interesting and useful. The least of which concern us with how people behave and how their behavior translates into public health practice, urban planning, education and communication. For me, the most interesting questions come when we understand what kinds of heterogeneity exist in populations. Understanding what motivates people to behave and respond differently is curious, especially when it relates to their cognitive capacities, their environment, and their learned behaviors. Thus we can begin to ask questions about how systems like architecture or policy, at very different scales, affect systems at other scales–like human reproductive choices for instance.

This study demonstrated that people aren’t really all that interesting in the movements, which is to simply say that we are predictable. We generally stay close to home or work and move in small bursts around these areas most of the time. Occasionally we make wider forays across the landscape.

There are privacy concerns to be negotiated. Many have been critical of the use of this information for the study. To my mind I don’t find the use of the data in the current study problematic for two reasons: 1) there is no identifying information available in the data, and 2) the mobile phones companies have been collecting this data, often out of legal obligation for billing precision, and using it for proprietary purposes with contractual consent from subscribers. I think it is important that some public good be made of the information, even if it means simply bringing to light the fact that these kinds of data are ubiquitously collected under the terms of cell phone contracts. Furthermore, a sample of people in the study explicitly consented to having their movements tracked as part of a value-added service, associated with navigation or weather for example.

Still, the study raises questions and begs for further social questioning and negotiating. I think where it starts to become problematic is when these studies begin to impede personal autonomy. Then again, the negotiations are where all the fun is…

BARABÁSI LAB

For a rundown on how the press is selling the story-via Google

Cellphone Tracking Study Shows We’re Creatures of Habit-NYTimes

Cell phone users secretly tracked in study-CNN

How Will Disease Spread?-ABC News

Mobile phones expose human habits-BBC

The story I was most interested in was Case Study 10: Environmental Monitoring with Mobile Phones (Ghana) carried out by Intel Research. I was struck by this paragraph, detailing the convergence of locative sensing and personal health status:

Another area for further exploration is the ability of mobile sensing to contribute to public health by linking health with environmental factors that have not been available before. For example, even though we know that there is a link between asthma symptoms and air pollution, previously it was not possible to directly correlate an individual’s symptoms with their exposure to air pollutants. Measuring people’s lung performance while measuring ambient air pollution exposure could shed new light on the links between air pollution and asthma, perhaps resulting in better treatments.

Clearly there are many thorny privacy concerns, but that’s the difficult (and fun) part to work out and begin to address.

Still, I think this example is on the mark in trying to link infrastructure, natural or man-made and population health patterns.