John Thackara : the future of service design (EN) from User Studio on Vimeo.
John Thackara, director of Doors of Perception, gives us his point of view on the future of service design in the public sector.
John Thackara, directeur de Doors of Perception, nous donne son point de vue sur l’avenir du design de service dans le secteur publique.
July / Juillet 2010
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.
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:
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).
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.
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.
Wright, S. (1977) Evolution and the Genetics of Populations. Vol. 3: Experimental Results and Evolutionary Deductions. University of Chicago Press, Chicago.
This is short presentation I gave to the Melton Foundation’s Symposium on Innovation which was held in Bangalore in August, 2009. I spoke on Innovation in Education, coming from the perspective of someone with the aim of bridging disciplines and interpretations.
This post consists of some notes that looking at the analogy of natural & artificial selection to design and its consequences. A worthwhile paper on a related but different topic is Christina Cogdell’s Products or Bodies? Streamline Design and Eugenics as Applied Biology (2003) Design Issues, 19(1), 36-53. doi:10.1162/074793603762667683
Types of Selection
The purpose of this page is to describe how natural selection can be used as a framing tool for recognizing how artifacts, services, and interventions can affect individuals and natural populations of humans and other species. The point is not to draw a direct analogy, but to try to link the effects of the things we make to the behaviors, growth, and flourishing of living things. These are not so much set rues as they are a set of guides that can help us reconsider the expected effects of changing our environment in order to evaluate the risk and alternative future possibilities involved in the production of technology from the most precursory to the most complex.
I was intrigued after a reading group discussion we had about anthropometrics. Wikipedia defines anthropometrics as the measurement of human to gather statistical data about the distribution of body dimensions in the population are used to optimize products. I would alter this definition slightly to say design products rather than optimize. Humans change and so do products.
We were a little unsettled by the focus only on human needs and the intent that anthropometry be entirely in support of comfort and ease of use. Taking a more critical approach, we started to brainstorm all of the different ways that design structures human and non-human behavior. We started to keep an eye open for ways that design and evolution can begin to interact. We hit some dead ends so I reached out.
I asked a group of colleagues if they knew of any comprehensive taxonomy of selection, and here is what one of them (Joel) contributed:
There are so many different ways to split selection up that it can be mind-boggling. To make it worse, those who study molecular evolution use different terms (positive, balancing) than those of us who study phenotypic selection. I don’t think there’s a way to taxonomize the terms satisfactorily, at least in a tree. It would probably look more like a convoluted Venn diagram.
That said, Joel laid out four areas that can be used to focus our attention. I’ve modified them from his interpretation, but they are basically agents, episodes, modes, and scales.
Here is how he originally wrote about it in his response to me:
In the phenotypic selection realm, I tend to split selection up in four different ways, based on agents, levels, fitness components, and mode.
The agent of selection, that is, the factor that causes fitness differences to arise, can be either ecological (phyisical or heterospecific) or conspecific. I would call the former ecological selection and the latter social selection (sensu West-Eberhard). The latter would tend to subsume sexual selection, which tends to be caused by male-male or male-female interactions. Includes frequency-dependent and density-dependent or other x-dependent.
The level of selection describes the units that exhibit fitness differences (which, annoyingly, Gould call “agents” of selection). This can be individual selection, and at higher levels, family selection, group selection, kin selection, social selection (sensu Wolf, Moore, and Brodie), etc. Hard and soft selection can fall under this category as well–Wade and Goodnight have good papers discussing this.
Third, selection can be split into different “episodes” by splitting total fitness into multiple components. This is usually done because it is empirically convenient, or to examine evolutionary trade-offs. This gives rise to terms like survival selection, fecundity selection, and sexual selection.
Finally, you can describe selection based on the shape of the fitness surface, i.e. the “mode.” This includes directional (linear), stabilizing, disruptive, and correlational (all three quadratic). Of course, the shape of the fitness surface is often complex, and you can have elements of all of these going on at once when you’re considering multiple traits.
Reframing Selection
We might think about what Joel said differently and transform it as the grammatical structure of a sentence. Where:
AGENT = SUBJECT
SELECTION =VERB
EPISODE = DIRECT OBJECT
MODE might be akin to diagramming the entire sentence. while SCALE is more like the context that the sentence takes place within (e.g. the paragraph or passage).
Agents
Agent refer to the most causal explanation for the response to selection. Agents provide the mechanistic explanation and frequently are the antagonists to the entity/entities experiencing the effects of selection.
From a designer’s perspective, these agents should be the artifacts or services we create either with the intent to exert some selective force or ameliorate it.
We can understand these as ecological agents that affect anything from the climate of our surroundings, our food supply, the structure or our living and working spaces, interactions with outer species (as in pets, disease, or domesticated laborers), and even perhaps to our conventional definitions of time that enable further articulations of the environment.
Similarly social agents work along the lines of our own perception, learned, and innate behaviors to enumerate male-male, male-female, family, and cooperative interactions. Sensation and display are extremely important because they distinguish among individuals to allow decisions about how to interact. Social agents range from clothing, jewelery and other status symbols to weapons, traditions, and business plans as agents of cooperation or competition.
There is a nice hybrid space too where ecological and social meet in the production of artifacts favoring or disfavoring reproduction–in vitro fertilization on one hand…and condoms on the other, for example.
Often, agent-based selection is described as selecting for trait ‘x’ and can even be more complicated when traits x, y, and z covary as a result of this selection. As a consequence we find that selection can be multi-facited and not reducible to a single interaction. Hence, we need to reconsider the cumulative effects of each agent’s contribution.
Episodic
Moving along the causal chain (if we can indeed identify it), we would then want to understand the factors or physical attributes that are on the receiving end of the agents’ work. From an empirical perspective, this is often where conflict begins and fluctuates in an ever-present set of trade-offs. We can split the effects into many different components looking at reproduction, lifespan, health, outlook, social status, niche, range, communicativeness, and, perhaps most importantly, agency (as the ability of an individual to act as its own agent).
This is the main point of interest in design–i.e. what, where, when do the effects of the design work manifest in nature?
Mode and Variance
In order to understand what patterns are present, evolutionary biologists look at variation and the response of a particular trait or episode to selection from agents. Here attention is focused on the values of the entire population in contrast to just the trait itself. We can certainly use these visualizations and modes to describe the distributions of episodic traits, but here there is explicit quantitative emphasis on the response to selection over one or more generations.
We can think about it in different ways: populational and interactional or hard and soft.
Populational
Populational patterns include the ideal types of directional (linear), stabilizing, disruptive, and correlational (all three quadratic), and null (no selection). The shape of the fitness surface is often complex, and you can have elements of all of these going on at once when you’re considering multiple traits.
The graph below is composed of four of these types where the axes show the proportion of the population as a function of trait values through time. The shaded areas represent the part of those populations that is being selected for. The color simply differentiates between types. Correlational selection is not shown because it consists of the interaction of multiple traits in response to selection, and we would need a 3-dimensional graph to show just two of those traits changing.
From the graph you can see the response of a population to null selection. Because mutation-based variation is not selected out of the population, the shape of the distribution randomly changes and will not fit a “standard” distribution.
The blue, directionally-selected distributions move (you guessed it) directionally because of pressure against one end of the distribution.
Likewise, stabilizing selection in green is like directional, but instead of one end the pressures are exerted to stabilize the mean.
For diversifying (aka disruptive) selection in red, the mean is selected against–leaving greater proportions near the previous tails of the distribution.
Interactional
The second way is what I call interactional, meaning it depends on the interactions among agents, often in space. Here, ecological agents and social agents exert their effects. The goal of this description is to capture the meaning behind the mechanism (thus, interaction) rather than the change over time. When coupled with population visualization techniques, one begins to get a dynamic picture of evolutionary change.
We may be able to consider correlational selection as a special case of interactional (and not populational) because the internal constraints within a population’s gene pool and genomic regulation are effectively a suite of internal genetic interactors at a different scale.
Normally however, we can think of interactional patterns in terms of frequency-dependence, density-dependence, or some other x-dependent factor related to ecological or social agents. So frequency-dependence, for example, is just a way of describing the total effect of agents…or of saying that the trait in question responds in a way that is frequency-dependent.
The main difference is that interactional describes the mechanism of selection itself (within a generation), while populational describes the response to selection (change between generations).
Putting the two together means we could graph dynamic change.
Hard
Both hard selection and soft selection are relative to the population as a whole. Hard selection is like a bat chasing an insect. The insect has some maximum speed that it can flee and the bat has some speed that it can chase. Assuming it is only the bat and the insect, then there is hard selection for the speed at which the insect can flee.
Soft
Of course insects are probably not alone since they tend to aggregate in large populations. Soft selection takes this into account and considers the effect of more than one insect fleeing. Here the insect needs not be faster than the bat, just faster than the other insects that the bat is following!
The major difference is one of absolute value or of percentage. Hard selection works on the absolute value of a trait while soft selection works on a percentage of the distribution of trait values.
Scale
The scope of impact of a particular service of artifact is also important, especially when we ask the question, “for whom?” Is it working on a emergent trait or even creating one? Examples might include political systems or policies that increase or decrease emmigration, the locating of a hazard that increases mutation rates, or one child policy.
Whereas before we were only considering a single ideal population, what happens when we include multiple populations? Does the work of the designer or design team affect traits that span across individuals and include qualities that can only be formed from collective-action?
Some levels of scale might include: individual, family, kin, group, social, community, or ecological.
The word mitochondrion comes from the Greek μίτος or mitos, meaning thread and χονδρίον or chondrion, meaning granule (thanks! wikipedia). But this isn’t about the mitochondrion itself. Rather, this is a story about how the genetic information that helps mitochondria reproduce and silk threads are rewoven together.
What is a mitochondrion? It’s an organelle (kind of like an organ in your body) for a cell. They generate much of the chemical energy used by a cell to carry out its different processes.
I have been working on a project for the last few months that extends work on what I call Silking Systems. By calling it Silking Systems, I’m trying to emphasize the patterning of silk and textile production as a set of relationships, things and interactions to accomplish varieties of silk/non-silk relationships, rather than as modes of behavior or production which are static – or should I say pre-threaded?
In 2008, some of my students researched How Silk is Made (after How Stuff is Made) for my class on Design for Sustainability. Their work documents the collection and processing of the silk fiber from cocoons to the thread you find in finished textiles.
About a year later, I worked with students at CEMA to develop square cocoon. Yes, a square cocoon. However, we also succeeded in learning a lot about sericulture – the raising of silk moths and worms – for silk cocoons which are then turned into thread. You can see some of process for making a square cocoon – as well as a lot of other aspects of silk production – in this flickr set documenting some of our work on Silking Systems.
In attempting to learn about sericulture from scratch, I visited some local producers in Karnataka, India and pulled in some textual research and advice – including Joseph Needham’s classic series on Science and Technology in China (1998 ed).
The most recent concept that I want to document here is pretty simple. Human mitochondrial genome sequences are woven in sequence using silk to produce a pattern that matches the mitochondrial nucleotide patterns.
Before I go further, I should acknowledge the assistance of Ashwathnarayan who aided me tremendously is becoming knowledgeable about silk production and weaving. He also did all of the weaving by hand with some help from me in reading the sequence. Nonetheless it was a true collaboration throughout. David Matthew was also instrumental in helping to build some of the loom pieces as well as providing emergency translation from Kannada to English when my conversations with Ashwathnarayan became difficult or too complex. At the beginning too was Millie who accompanied us to a silk production house in Vijayapura, Karnataka – just north of Bangalore. Millie did some great translation acrobatics using her English and knowledge of Tamil to translate for me and to speak with Ashwathnarayan – who in turn was speaking with the silk producers in Kannada.
I have a few implicit goals and a few explicit ones as well. An implicit one is that I am attempting to push the relationship between craft, production, economic agency, and hybridity. I am drawing to some extent from the idea that economic value is generated through recombination – that goods and/or services emerge and create value when they are mixtures of other (especially unrelated) things.
Transferring the silk thread for the weft from Gabriel Harp on Vimeo.
Eric Beinhocker details this concept of value through hybrids along with an evolutionary algorithmic perspective on economics in his book The Origin of Wealth (2006). The book was recommended to me by Cesar Hildago, a Research Fellow at Harvard University’s Center for International Development. Cesar’s work on complex networks has also influenced this project, starting with his article on the Product Space of Nations (2007) and continuing with images like figures 1 and 2 which came out of his research. The network graphs make it easy to see how different economies differ in the products they export.
My thinking is that by challenging some aspects of the status quo in silk and textile production, new value propositions might be found. This comes, perhaps, by demonstrating that square cocoons are possible or by remixing molecular genetics and weaving to create a series of silk stoles based on a mitochondrial haplotype found frequently in southern India.
Preparing the shuttles from Gabriel Harp on Vimeo.
Another goal is to simply visualize the mitochondrial genome – and to make it as accessible for teaching and learning as possible. Making it tactile and making it in silk allows people to touch, feel, and to see individual sequence variation. Silk thread is a good scale for this sort of thing – not too small and not too big either. So in viewing these stoles (which measure about 5 meters each in length) one is challenged to look for patterns and they are rewarded with the same.
The process is pretty simple. I started with the stored Genbank sequence of the M2 haplotype which is traceable to early settlers of India. I took the nucleotide sequence information (atctcgctagatagacat, etc) and printed it out in BIG type so that we could follow the pattern easily. By assigning a color to each base type, patterns will reveal themselves. For our first prototype, I chose yellow, blue, green, and red. These are used commonly in genomic sequencing and prediction software (at the University of Michigan, for example) and I wanted to start with something that would resonate with biologists and would also suggest a playfulness associated with childhood and formative development.
Weaving silk using a mitochondrial sequence from Gabriel Harp on Vimeo.
Weaving the pattern is excruciatingly slow. In fact, this kind of work goes against a lot of how silk waving is organized from a production standpoint. There are no repeated patterns and each thread is individually sequenced – that’s the point! We accepted that we might introduce our own errors into the fabric, but then that fits well with the concept; as we try to speed up we might lose fidelity with the original sequence. There are a handful of good correspondences between the weaving process and DNA replication, and they are themselves teachable moments for students that encounter the project. It also gets them thinking critically about what correspondences do or do not exist, as a way of developing their own comprehension.
I’ll expand this article as the project develops further, but I’ll end now with one nagging curiosity. The pattern that is being produced is engaging and pleasing. It makes me wonder if it in some ways exploits a bias we humans may have towards certain arrangements. Specifically I’m thinking about pink noise patterns…but I need to search more.
References
Needham, J., & Kuhn, D. (1988). Science and civilisation in China: spinning and reeling. Vol. 5. Chemistry and chemical technology. Pt. 9. Textile technology. Cambridge University Press.
Beinhocker, E. D. (2006). The origin of wealth: evolution, complexity, and the radical remaking of economics. Harvard Business Press.
Hidalgo, C. A., Klinger, B., Barabasi, A., & Hausmann, R. (2007). The Product Space Conditions the Development of Nations. Science, 317(5837), 482-487. doi:10.1126/science.1144581
Philip Zimbardo conveys how our individual perspectives of time affect our work, health and well-being. Time influences who we are as a person, how we view relationships and how we act in the world. Via RSA
Disaggregation among natural and social scientific communities can lead to misunderstandings about the different components of disaster management and socio-ecological systems. Terms like resilient, adaptive, robust are often used to describe systems and their processes and come up in the literature, policy, and the media very frequently. They have catch my attention because they have different use patterns in the field I know a little about: biology.
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.
Nisbett, R. E. (2004). The Geography of Thought: How Asians and Westerners Think Differently…and Why. Simon and Schuster.
It’s not often that unfunded proposals make their way into disinfecting daylight. Sometimes you try again, and sometimes you just let them waste away among the dusty electrons of your hard drive.
I don’t know which category this one falls into, but I do feel it’s worth sharing and making public. Perhaps someone will even comment with improvements. I can only hope.
In any case, this proposal was dependent on a constellation of partnerships (and funding) to make the project move forward–at least from my perspective. Sometime a little cash can help develop needed projects and spur collaboration. This was a submission to the Knight News Challenge which is supposed to announce its winners sometime in mid-June. Since I know I’m already out of the running, there isn’t really a compelling reason not to share—but please tell me if there is!!!
Anyhow, here is most of it—-minus some names to protect the innocent—–except one: this logo was created by Zack Denfeld, and we’ve used it on a variety of projects. For more, you should visit his launchpad.
Describe your project:
Envirocasting adapts global weather information to the cultural and operational needs of local [international disaster preparedness organization] branch offices and communities, supporting their risk assessment and preparedness needs. A wealth of information exists to support disaster preparedness, but a gap exists between the design of information services and their local use-contexts, limiting widespread use and effectiveness. The benefits of these information services are clear to local decision makers, and they are anxious to put the tools and news sources into practice.
However, exposure to digital news platforms is low, and the capacity to use them in decision making contexts is minimal as a result of this disconnect between design and use.
Envirocasting takes a design anthropology approach to inform the design, distribution, and acquisition of digital weather information services to local decision makers. Design anthropology seeks to understand the role of design artifacts and processes in defining what it means to be human. Using this approach, local patterns of information consumption and culture related to futures, information design, and technological metaphors can be identified, allowing for the design of appropriate services. Design principles as well as specific, local use-applications will aid in the distribution and assessment of weather forecast efficacy. Thus, weather news for risk assessment can flow more precipitously to decision makers, allowing them to coordinate the disaster preparedness efforts more quickly and strategically.
Simulation games for local communities will support learning and the application of information services in context. This provides use-case memories of the future and practice in managing uncertainty with minimal risk.
How will your project improve the way news and information are delivered to geographic communities?
Envirocasting aims to localize climate information by making it simple, non-technical, clear, easy to use, and as meaningful as possible. Maps are relevant when their colors, numbers, icons, and scales are relevant and supported by culture and context. Information that connects with specific actions can be used confidently in planning and decision making. Specific use-cases communicated by local communities will drive the development process and will help weave the digital media fabric with aesthetics, narratives, and metaphors. Games support critical thinking and social play to help decision makers and communities explore the dynamics of news and information-based decisions for climate-related disaster preparedness.
How is your idea innovative? (new or different from what already exists)
Envirocasting innovates by translating connections between design and use. When local conditions refract the design and dissemination of information from distant or multiple sources, innovation is an inherent byproduct. Envirocasting is designed with the mind in mind, understanding cultural legacies that influence the recognition of uncertainty and metaphors. It bridges experience, play, and interactions, creating memories of the future. The project identifies appropriate implementations of open-source digital information services and defines a set of prescriptive resources for innovating across disaster risk contexts and cultural processes based on abstractions and lessons from six local communities in three countries.
What unmet need does your proposal answer?
A fact-finding mission conducted surveys, interviews, meetings and workshops over two-month periods in 2008 and 2009.
Explicit unmet needs include:
And don’t take my word for it:
What will you have changed by the end of your project?
More-Measurable outcomes:
Less-measurable outcomes:
How will you measure progress and ultimately success?
The uses of weather and hazard preparedness information can be measured using surveys, interviews, meetings and workshops and compared to current estimates of use and use cases, but those data are useful differently for different people including the decision-makers, their constituents, their supporting agencies, and funders of this project. Thus, we intend to cast progress in varied terms for the different stakeholders and partners.
Some of these guiding questions include:
Success, on the other hand, is more elusive. Disasters are sporadic and may not always afford a direct link between information effectiveness and risk reduction. However, existing case studies show that these types of information, when combined with specific actions, can lead to significant reductions in both the vulnerability and negative effects of a disaster such as flooding. The key to assessment it to engage in a continual processes where we value choices and transitions in practice. The design of this project take into account the high-stakes involved in the decision-making and information uses by providing opportunities for both high stakes (post-hazard) and low stakes (simulation-games) assessment.
Do you see any risk in the development of your project?
The biggest risk at present is that the organizations listed do not have a history of working together (this is indicated by the generic names rather than their proper ones), but this is also where the opportunity exists. The leadership (particularly of the larger orgs) is wary of their participation in the project without first-hand knowledge of all partners and/or certain funding. This conversation is ongoing at the time of this application and continues to develop. If the proposal moves through to the next round, we should at that point be able to name each of the partners in more specific terms.
Supply-side risks (design-mediated)
Demand-side risks (user-mediated)
What is your marketing plan? How will people learn about what you are doing?
The conduits for marketing are, in many respects, already in place. The organizational structure and extent of [intl. disaster preparedness agency] branch offices will facilitate branding and distribution using existing networks of community organization, tactical planning, and response offices. Though the value of the services should be self-evident in the design and cognitive acquisition of the services, the goal is to help users to practice using and applying these information services. We also recognize that aesthetic values can elevate the recognition of value and the maintenance of that value through everyday use. Thus, arriving at these values will be a principle objective for all participants.
In order to increase domain knowledge, the outcomes can be shared among the participants, their centers, and via professional and interest networks including the design research community which actively engages with similar project goals. Because some of the project partners include university centers, schools and research organizations, the outcomes will be shared with emerging professionals including graduate students and visiting fellows.
Tactically, the marketing plan for simulation game-based training is slightly more difficult because it requires additional preparation, training, and presentation. Nonetheless, with a bit of effort, these games will reinforce the marketing strategy for the primary goal of adapting weather information using the same local community branch office network structure. We also expect to develop videos that demonstrate our process as well as the use and value of the informations service under construction. But ultimately, the best marketing will be the effectiveness of the adaptation process.
Is this a one-time experiment or do you think it will continue after the grant? If it is to be self-sustainable, what’s the plan for making that happen?
Envirocasting is the application of a process to translate meaning across cultural contexts with relevance for local concerns. We do not view it as an experimental process so much and an underutilized one. Luckily, there are many resources, case studies, and additional expertise to draw from in the process. Our goal is to assemble them and to draw the pieces together into relevant platforms and prototypes for weather information services.
The project will accomplish this goal as a one-time research project that will publicly document its methods and outcomes as guides so that they can be applied in new use-contexts and for wider information arrays. We fully expect that the different project partners will continue to apply the work and experience in varied ways after the initial project, although they may carry it out to their own ends.
Our method for fostering rhizomatic-like dissemination of the results (and thus, sustainability) is to link with additional strategic partners whose networks span varied social groups, languages, use-contexts, and concerns. Furthermore, the acquisition and integration of the research (as well as the information services it supports) can be broadly advocated from a policy perspective because successes arise from its application and benefit in specific, local communities. The overall plan for sustainability is to demonstrate that these information service platforms reduce risk by enabling decisive action before pending hazards become disasters. If this is demonstrated, sustainability will ensue, even if not in the form described in this proposal.
Disasters are a combination of cognitive, social, infrastructure, and ecological failures. Preparation in each system helps to create buffers to provide resilience within each system that can in turn translate to resilience in each of the other systems. Thus, trends, impacts and the frequency of disasters are often amplified by the interactions between different social domains, resource bases, and locations.
Key requirements for recognizing trends in disasters include being able to:
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
After writing yesterday’s post on psychology and climate change, I stumbled upon this article from the journal Ecological Economics entitled, “The art of the cognitive war to save the planet”.
The article details the proposition that our adaptive capacity–to respond to environmental feedback–to learn–is structured by the double bind, a concept coined by Gregory Bateson. A double bind is when an individual receives conflicting messages (intransitivity of preferences?) that disallows action on their part because responding to either message means being in conflict with the other. Wikipedia has a more detailed description here, but Bateson’s articulation of the concept can be found in Steps to an Ecology of Mind (2000, University of Chicago Press).
The author’s argument is that sustainability, or human-environment interactions that respond dynamically to each other, is constrained because beliefs about oneself and the community are increasingly biased towards individual level sustainability for two reasons. First, individual safety is increasingly linked to individual performance. Second, alienation from environmental feedback loops means that an amplification of uncertainty is taking place resulting many more belief ‘nodes’ about systems level relationships. This amplification results in greater propensity for conflict to develop between an individual’s assessment of the environment/system and their own well-being.
The task they outline is manifold–having many forms and elements. It means developing a shared cognitive base from which to develop mental models for collective action. The goal of a shared cognitive base is to help connect system level safety ideals to individual level belief nodes They argue that to do this requires “simple messages with the potential to shape individual belief systems”. Excessive information is to be avoided, while everyone should have access to the building blocks of conceptual blends that synthesize complex information.
The authors, Antal and Hukkinen, argue that more direct and influential injunctions should be exchanged to help reframe the context towards systems-individual linkages–not just individual. Thus an injunction, “Become a vegetarian” becomes the positive injunctive norm, “Become a vegetarian to maintain the status quo” and then makes more sense in terms of promoting sustainable behavior when coupled with a positive injunctive future norm, “Become a vegetarian so our civilization can survive.” This tactic seems similar to one described in the book Nudge (Thaler and Sunstein, Penguin Books, 2009) where they describe some forms of social nudges based on experiments in judgment and decision making.
Thaler and Sunstein describe how some forms of social nudges unfold. These include:
The example of the positive injunctive norm seems to be what Antal and Hukkinen are advocating, but with a touch more bite.
Their case lies in creating cognitively accessible links between systems status and individual experience. An example of this might be an electricity brownout linked to CO2 accumulation or perhaps a full blackout each time species diversity is degraded.
Their conclusion that ICT services are needed to help these links form is predictable. Systems like smart grids, early warning systems, and other membership and signaling tools are appropriate, but the burning question is how to implement them in society where the tools themselves do not reflect the normative values.
One scenario I had after reading this is a case where an electrical power generation company that is responsible for supplying the city creates more direct informational links with its consumers. Neighborhoods in the city already experience frequent and irregular cuts in supply. Engineers, particularly in energy, tend to focus on maintaining supply based on certain assumptions. Sometimes we don’t always know what those assumptions are. Smart grids have been identified as a solution bridging consumption and supply (albeit from a supply perspective), but what if there was a more jugaad solution?
I am hereby coining the term Ener-geets™ to describe a form of information transfer between energy consumers and energy suppliers. Let’s say consumption is pretty high. It’s hot. Everyone has fans running, AND the big cricket match is on. Power suppliers have decisions to make in order to maintain a consistent supply, but what if they could provide realtime feedback to their customers that threshold levels were being reached and if their behavior didn’t change, they might loose the ability to follow the cricket match to its conclusion.
Cut the normal means of feedback out for the time being (an energy bill or brownout) and allow the power operator to send a message, perhaps in the form a tweet (from Twitter), to everyone following those tweets. Potential overshoots to the grid capacity could be avoided. But then, this would go against established channels of information flow and place a great deal of responsibility in the power operator’s hands–er..mobile phone.
To connect the feedback loop, individual consumers could also be sending messages, informing of power cuts, potential spikes in use (a festival perhaps), or other changes or observations about consumption at the individual level.
You start to get the picture. Now, how do w do it?
Ref: Miklos Antal, Janne I. Hukkinen, The art of the cognitive war to save the planet, Ecological Economics, In Press, Corrected Proof, Available online 3 February 2010, ISSN 0921-8009, DOI: 10.1016/j.ecolecon.2010.01.002.
(http://www.sciencedirect.com/science/article/B6VDY-4Y9HP0Y-2/2/8effb7b70d90787bc2250323ffeef134)
Keywords: Human-environment interaction; Belief systems; Environmental strategy; Climate change communication; Cognitive studies
