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.
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
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.
This is one of the best popular articles I have read on the psychological factors affecting individual and group decision making in complex, high-stakes uncertainty. The focus of this article is on climate change, but the implication can be translated to other problems just as easily. This is simply because of the scale and the way that problem itself is generated. The scale is large and usually prohibits people from seeing the impacts of decisions, while it is also caused by many individuals making choices that contribute to the problem.
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 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.
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.
A letter to this week’s Nature describes a study that reveals an interesting model of human movement patterns. The study is the first of its kind for the simple reason that the researchers were able to objectively track people in the natural environment by using mobile phone locations as proxies for their movement.
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…
This is an interesting report I came across from a UN-Vodaphone partnership designed to provide “research and recommendations on how to use technology and telecom tools to effectively address some of the world’s toughest challenges” (found via THDblog)
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.
I was up this morning thinking about the kinds of spaces, communities and interactions I would like to see. Somewhere between physical computing, synthetic biology, evolutionary ecology, and design is a space where species can speak and be recognized by each other, where urban infrastructure becomes adaptive in the space of days and not decades, where the threshold of difference is lowered to such a degree that new networks between otherwise unrelated groups and individuals can find common ground.
Perhaps for the first time, I am beginning to see how things can be connected for the purpose of builing empathy. Whereas previously, I think the difficult work of etting to know a species was largely out of many peoples’ desires and time banks, perhaps there are now ways of making the opportunities both immediate and resource-efficient.
Rather than always seeking to decouple tightly-linked host-parasite relationships, can we find ways to make new ones…perhaps ones that can grow into mutualisms and symbioses? Is hardwiring a step in the process? What are the costs, benefits, sources and sinks? Can we create or link networks of co-dependence? What models of covariation should we adopt: linear, dominance, epistatic, topological?
Cooperation and mutualism among humans and other species has spanned the landscape for thousands of years. This is particularly evident in the silk industry here in the southern Indian State of Karnataka where almost every woman wears a silk sari. The silk industry in Karnataka is massive. Visitors here will find silk shops on most main streets. The city of Mysore is one very well-known production center for silk (akin to Bordeaux for wine or Darjeeling for tea), and although Karnatakan silk production has fallen in recent years (perhaps due to development and water shortage), it still accounts for almost 50% of India’s total silk output.
This semester a group of my students undertook the task of documenting the silk production process as it occurs in Karnataka. They visited several sites ranging from a rural handloom enterprise to industrial mills and retail outlets. They prepared themselves by looking at precedents from similar art and design students looking at how things are made. They also focused their investigations by first reading the Design for Sustainability Guide. In this way, they managed their engagement for the purposes of producing actionable knowledge to foster sustainable design practices.
One of the outputs of their research is this account of silk production. I found it detailed, well-researched (though I would have preferred more footnotes and cited references), and informative. I think it also illuminates the degree to which these students understand their processes and are willing and able to identify parts of the systems for further exploration.
Cooperation and mutualism among humans and other species has spanned the landscape for thousands of years. This is particularly evident in the silk industry here in the southern Indian State of Karnataka where almost every woman wears a silk sari. The silk industry in Karnataka is massive. Visitors here will find silk shops on most main streets. The city of Mysore is one very well-known production center for silk (akin to Bordeaux for wine or Darjeeling for tea), and although 
One of the outputs of their research is this 
