Insights on the Architecture of Collaborative Design

June 28, 2011 at 2:05 AM · Filed under architecture, community interaction design, Design, design ecology, service design

I recently visited Stanford University’s school of design.  They have put a lot of effort into uncovering how infrastructure affects collaborative spaces for design use and practice, or rather, what design groups need to really succeed.  Click the image for a pdf (1mb) of insights they turned into enabling resources for collaborative and design activities.

Thanks to Scott Witthoft for his great tour! There’s also an article here from FastCompany outlining heuristics for generating better collaborative infrastructure.

Comments

The Shifting Balance of Design Practice

July 13, 2010 at 4:16 AM · Filed under architecture, biology, community interaction design, complex systems, Design, design ecology, evolution, interdisciplinary, network entrepreneurship

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:

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).

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.

Comments

The Taxonomy of Selection

June 13, 2010 at 10:07 AM · Filed under architecture, biology, boundary objects, cognitive justice, cybernetics, design ecology, evolution, futures, preferences, sexual selection

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.

Comments

Adaptation>Robustness or Plasticity>Resilience?

June 7, 2010 at 2:17 AM · Filed under architecture, biology, complex systems, cybernetics, design ecology, ecology, evolution, futures, genomics, interaction, interdisciplinary

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.

Comments (1)

Collaborative Workspaces That I Like (Redux 4: People Working Transparently)

May 31, 2010 at 8:09 AM · Filed under architecture, community interaction design

Most of what I liked about the GROCS lab at the University of Michigan Media Union was how the activities of various groups, classes and projects were in an open, shared space. Mix that with some movable furniture and a close proximity to other resources in the Media Union and it made for a good space to come up with ideas, share them and work them out with each other.

Comments

Collaborative Workspaces I Like (Redux 3: Two Stories of Fun, All-in-One)

May 31, 2010 at 8:08 AM · Filed under architecture, community interaction design

Ok, the pic isn’t great but you get the idea. This is “The Cube” at the MIT Media Lab. I visited the Lifelong Kindergarten group there and saw how their close proximity to tools, shared workspaces, and each other facilitated their work in progress. I really liked how the space was large with high ceilings, that it was a mess of projects, and that there was a table where lab members would work individually with a tacit sociality.

Comments

Collaborative Workspaces I Like (Redux 2: See-through walls)

May 31, 2010 at 8:05 AM · Filed under architecture, community interaction design

See-through walls at the Center for Complex Networks Research allow behavior to be observed while keeping conversations in common areas from interrupting focus. Shared offices help maintain an additional level of cohesion among lab mates.

Comments

Collaborative Workspaces I Like (Redux 1: Common Places, Close Workspaces)

May 31, 2010 at 8:04 AM · Filed under architecture, community interaction design, interdisciplinary

The common space at the Center for Complex Networks Research allows for group interaction, impromptu exchanges, and reception of visitors. Lunch, printing, library, and coffee all converge near conference rooms and shared offices.

Comments