This talk gave a broad overview of international issues and policies in agriculture and food security, and showcased three research projects that explore Agricultural BioDiversity, Genetically Engineered Crops and the difference between European and United States food laws.
Service designers identify and order goals in service systems. Service systems are a unit of analysis for an exchange of skills and capabilities which leads to the production of value in use (Vargo et al., 2008). Service systems are developed though the creation of value, where reinvention can transform the relationships of use and practice. Service systems are characteristically intangible, heterogeneous, simultaneous in production and consumption, non-perishable, and grounded in times and places that maintain their meaning and value (Kimbell, in prep).
One of the ways that designers understand service systems is by using a variety of approaches and concepts that isolate or concentrate focus on the relevant aspects of a system so they can drive experimentation and change. An example of this is a touchpoint, which means the aspects of the service are visible and come in contact with the users of that service (but see this discussion of its origins). You may have suspected that in a relationship of co-creation, touchpoints multiply quickly when production and consumption are linked since users are creators and vice versa. Another example that designers use is the line of visibility. This is similar to the touchpoint, and it describes what users see and experience in their relationships with a service system. It helps in rendering a system so that its processes and organizational structure are visible.
A draft diagram of a business process showing the line of visibility between the user and the organization dedicated to providing a service.
Because touchpoints and lines of visibility exist not only as tools but in practice, service experiences are tightly bound to tied to the production of narrative. Suspense in particular is a common experience for users when parts of a process, system, or set of relationships are hidden from view. Just imagine a time when you were the creator or recipient of a service. Much of your uncertainty or satisfaction was probably driven by what you knew or could expect about the outcome as well as the communication process that was taking place while the service was being delivered.
Richard Allen discusses suspense in his book about [Alfred] “Hitchcock’s Romantic Irony”. Allen cites Meir Sternberg’s distinction that, “suspense derives from a lack of desired information concerning the outcome of a conflict that is to take place in the narrative future, a lack that involves a clash of hope and fear; whereas curiosity is produced by a lack of information that relates to the narrative past, a time when struggles have already been resolved, and as such it often involves and interest in information for its own sake.”
So when working in service design we should decide if we desire to create curiosity or suspense and design our process accordingly. Allen also incorporates Ian Cameron’s view that suspense is a “channeling of emotions”. Clearly emotions can be powerful, but how and why? In Allen’s analysis, suspense is something that happens in us as we are forced to take up the prospect of narrative outcomes that are contrary to the ones we desire. Suspense is constructed out of moral uncertainty, balancing our expectations with potential outcomes.
Allen discusses Hitchcock and develops descriptions of two types of suspense: pure and impure. Pure suspense is broad and objective, prolonged by tension, delay, and narration that is unrestricted, moving between vantage points and locations. It leads to an anxious uncertainty and an increased expectation of a bad outcome as the deadline looms. Arbitrary delays segment time and increase the tension because a bad outcome seems close at hand. Often, the audience sees a threat before the protagonist and surprise happens through the manipulation of time. The outcome almost always favor of the moral victory, especially in popular media.
Impure suspense on the other hand is local and subjective. It is developed from points of view that provide different sources of knowledge often through the eyes of the protagonists and antagonists, keeping the audience informed while the characters remain unwitting. Deadlines are set early on and acceleration commonly heightens the alert attentiveness of the spectators who are active participants in the construction of the suspense. Knowledge is not made by the director. It is made by the audience in cooperation with the information provided to the characters. All too often, the audiences senses the outcome before the characters do by filling in blanks sources of meaning that haven’t been provided. Impure suspense favors empathy for the character, as if we were living through them. The moral outcome is less certain and often unrealized.
In order to try to make the differences between pure suspense and impure suspense more tractable, I imagined what users in a service system might say if they were experience one or the other. The result is in the chart below, and it adapts these distinctions and starts to resolve how one might go about implementing different narrative objectives for a service system.
Pure suspense
Impure suspense
Locations
I move unrestricted between vantage points and locations.
I stay highly local and subjective.
Points of view
My perspective is omniscient and wide-ranging.
I tell everyone what is happening everywhere.
I get different sources of information through the eyes of the others.
I keep some people informed and others in the dark.
Time
My day is prolonged by tension and arbitrary delay.
Deadlines are set early in the day and acceleration commonly heightens my emotional state.
Emotional states
I have anxious uncertainty and an increased expectation of a bad outcome as a deadline looms.
I am alertly attentive, experiencing empathy for others.
Knowledge Production
The person in charge chooses and focuses attention on the priorities.
I cooperate with the information provided to learn what to do next.
Expectations
I can explicitly identify a threat.
I am frequently surprised.
I sense an outcome before others.
I fill in blanks with sources of meaning that haven’t been provided.
Moral outcome?
I favor the best outcome – like what happens in popular media.
The best outcome is less certain and often unrealized.
References:
Vargo, S. L., Maglio, P. P., & Akaka, M. A. (2008). On value and value co-creation: A service systems and service logic perspective. European Management Journal, 26(3), 145-152. doi:10.1016/j.emj.2008.04.003
In the 1930s, evolutionary geneticist Sewall Wright pulled together research strands in the biology of inbreeding, the genetics of coat color in guinea pigs, statistical methods (including path analysis), and mathematics that codified the changes in gene frequencies in populations as a result of natural selection, mutation, and migration.
His resulting description of these threads set the stage for qualitatively different perspective on the evolutionary process. Wright described his perspective as a “shifting balance” model of evolutionary change, and it highlighted the role of small populations in the transitions between periods of high and low fitness. This pattern, which followed from his use of the term “drift”, describes the fluctuations of gene frequencies that result from the random sampling of small populations. This random sampling comes from mating in small populations that, because of chance, produces small deviations from the numbers of genes originally represented in the population.
Wright’s Shifting Balance perspective coincided with his introduction of the adaptive landscape as a term to describe the space in which random fluctuations of gene frequencies in small populations could push the populations away from adaptive peaks or periods in which they were reproductively successful, and which would in turn allow natural selection to push them towards new adaptive peaks – areas of differential reproductive success.
Though Wright’s perspective on evolution is controversial (in a generative way), the perspectives and tools that emerged from his ideas have endured. For example, Wright’s work preceded algorithmic approaches to optimization problems in mathematics, networks (traveling salesman), metallurgy (simulated annealing), and artificial intelligence – to name a few
The process of Shifting Balance is described as a series of three dynamic phases:
Phase 1, the exploratory phase, the action of small groups explores new combinations. Most stay on the suboptimal fitness peak (reasonably successful), but some get caught in adaptive valleys (unsuccessful).
In Phase 2, selection causes the groups that are in the adaptive valleys to move toward new, higher-fitness peaks.
Finally, in phase 3, groups at higher fitness peaks send off migrants helping other groups move to higher fitness peaks.
Phase 1: The Exploratory Phase
Phase 2: The Selection Phase
Phase 3: The Migration Phase
While Wright’s process was intended for population genetic systems, an increasing convergence between social processes, cognitive psychology, technology, ecology, and creative practice suggests that the concepts apply well to the exploratory, form-finding processes that precede the design and production of materials and services. The implementation of the Shifting Balance process as a analog for social and creative strategy is useful for the production of highly original and robust creative solutions – or, at least it’s a testable hypothesis.
For some, analogies between biological and social processes are difficult to comprehend. However, the design of services and interactions is dependent on the ordering and reordering of processes, materials, people, and ideas. Combinations and recombinations of these things, when developed thoroughly and communicated, can impact the delivery and relational aspects of individuals working in cooperation or separately.
We could envision this process as a sort of charette (period of intense design in collaborative groups) activity where:
The exploratory phase initiates adaptive schema (creative combinations) which are driven by the interactions, specializations, and diverse perspectives of small groups;
Intergroup selection resulting from evaluation, the inherent heterogeneity among groups, and intended service platforms begins the iterative process of amplification of good combinations;
Export and translation of valuable forms/schema to other groups in order to test them against different problems, social contexts for cooperation, and consumptive patterns.
The immediate benefit of this strategy is the demonstration of expertise in practice, the role of discourse, and the chance events that can drive innovation. Participants from different disciplines will have to opportunity to observe and engage in creative problem solving within highly diverse communities. Here the focus is on collaborative ideation followed by problem-solving across disciplinary and expertise-based boundaries and ultimately an exercise in cooperative translation, storytelling, and communication.
There is enough social scientific research to at least point to the benefit of diverse groups, although it would be worthwhile to have a better handle on an ideal number – i.e. what counts as a small population. Plus, how do we go about choosing? What is the process of selection…or should we instead be saying, “What is the process of attachment?” And finally, are there specific patterns of translation or dissemination that we should aim for? For if migrants endowed with the most successful schema do disperse and link up with others, they have an opportunity to cooperate and raise the capacity the other groups elsewhere. But through which mechanisms to we initiate and implement these processes?
There are a few other ideas that seem uniquely coupled to the Phases of Shifting Balance. An example is the goal of participation as a unique form of empowerment in community planning exercises. One particular model of participatory engagement provided by Conde et al. (2004) is used in the context of climate change planning (below).
The Landscape of Participation
This example shows transitional categories in participation. When viewed through a model of culture which emphasizes process over characteristics, these are skills acquisition categories that indicate differences with an impact on fitness – i.e. reproductive success.
Each category represents a different level of engagement, a level that itself suggests a tighter relationship between participants and the tools of participation or cooperation.
Informative participation is an exchange of information, which may or may not be meaningful.
Consultation requires that participants begin asking questions as well as providing information.
Functional engagement means that different participants identify and agree to share goals, thus ordering their actions in accordance with each other.
Interaction means the initiation of feedback, where signals and shifts in the participation is met with responsiveness and dialog with the others.
Self-motivated participation is demonstrated by the points at which processes are acquired and reorganized by the participants themselves.
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.
Wright, S. (1977) Evolution and the Genetics of Populations. Vol. 3: Experimental Results and Evolutionary Deductions. University of Chicago Press, Chicago.
The Distribution of Intellectual Property Claims on the Human Genome. Source Data: Jensen and Murray (2005) Intellectual Property Landscape of the Human Genome. Science 310:239.
Click on the image for a Processing animation of patent locations.
Approximately one quarter of human genes are protected by intellectual property regulations. Little information about the number and distribution of gene patents is available in a manner empowering to members of the public. Existing gene patent resources rely almost exclusively on verbal search strategies for access in contrast to visual interfaces that promote exploration and discovery. This can be traced to the relative immateriality of genes which cannot be seen and whose effects are experienced through a web of medical, environmental, and social constructors.
One solution to this problem is to create a visual map of patent claims in the human genome. By representing the location, number, functional, and patent characteristics of genes, such a map could provide immediate visual access and cues for further investigation. Maps are created through the contributions of multiple constituencies and exist as objects for discussion, reflection, and mediation. Using patent data from the human genome developed by Jensen and Murray (Science 310: (2005) p239-240), we have started this project as a series of creative sketches. CAMBIA continues to update these data in accordance with current information.
Genes involved in human health, disease, and drug discovery tend to be heavily patented. A map would provide reasonably accessible information to non-specialists and help to scaffold conversations surrounding these issues. It is helps to document regions of positive selection, where specific genes are being disproportionately valued, by social and technological actors operating on human and non-human life processes.
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.
This graph depicts four abstract types of natural selection. The colors are used only to differentiate between types. The axes show the proportion of individuals in the population as a function of their trait values through time.
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.
If you are willing to accept some non-human based research in pursuit of the human, you might find these helpful. They have a pretty heavy biological and philosophical bent to them. Some like Gablik, Miller, Darwin, and Dissanyake provide pretty sweeping theories for the evolution of art and design (with varying levels of detail and different forms of evidence). Others deal mainly with theory and
research derived from observation of non-human animals. Still others use social science and humanities based approaches to the question (Loos/Danto (with Gablik replying in ‘progress in art’), Bergson, Luhmann). Also see Bobbi S. Low’s cite for what may be the only scientifically testable prediction in the bunch.
Donath, J.S. Signals, Truth and Design. MIT Press, Cambridge, MA, forthcoming.
Burke, E. 1757. A philosophical enquiry into the origin of our ideas of the sublime and beautiful. R. and J. Dodsley, London.
Endler, J. A. 1992. Signals, signal conditions and the direction of evolution. American Naturalist 139:S125-S153.
Gablik, S. 1976. Progress in Art. Rizzoli International Publications, Inc., New York.
Kirkpatrick, M. 1982. Sexual selection and the evolution of female preference. Evolution 36:1-12.
Miller, G. F. 2001. Aesthetic fitness: How sexual selection shaped artistic virtuosity as a fitness indicator and aesthetic preferences as mate choice criteria. Bulletin of Psychology and the Arts 2:20-25.
Ryan, M. J. 1990. Sensory systems, sexual selection, and sensory exploitation. Oxford Surveys of Evolutionary Biology 7:157-195.
Scheib, J. E., S. W. Gangestad, and R. Thornhill. 1999. Facial attractiveness, symmetry, and cues of good genes. Proceedings of the Royal Society of London B 226:1318-1321.
West-Eberhard, M. J. 1979. Sexual selection, social competition and evolution. Proceedings of the American Philosophical Society 123:222-234.
Loos, A., & Opel, A. (1997). Ornament and Crime: Selected Essays. Ariadne Press (CA).
Ellen Dissanayake, Art and Intimacy: How the Arts Began (Seattle: University of Washington Press, 2000),
Christy, J. H., and P. R. Y. Backwell. 1995. The Sensory Exploitation Hypothesis. Trends in Ecology & Evolution 10:417-417.
Laland, K. N. 1992. A Theoretical Investigation of the Role of Social Transmission in Evolution. Ethology and Sociobiology 13:87-113.
Miller, G. 2000. The Mating Mind: How Sexual Choice Shaped the Evolution of Human Nature. Doubleday, New York.
Nettle, D. and H. Clegg. Schizotypy, creativity and mating success in humans. Proc. R. Soc. B (2006) 273, 611–615
Kavolis, V. Community Dynamics and Artistic Creativity. American Sociological Review, Vol. 31, No. 2. (Apr., 1966), pp. 208-217.
Luhmann, N. Art as a social system. Stanford University Press. Stanford, Calif. 2000.
Network Theory—the Emergence of the Creative Enterprise. Albert-László Barabási. Science 29 April 2005:Vol. 308. no. 5722, pp. 639 – 641
Low, Bobbi S. 1979. Sexual selection and human ornamentation. In Chagnon, Napoleon A., and William Irons, eds., 462-87. – describes a test of sexual selection for art as the comparison of stable versus unstable symbolic systems
Danto, A. C. 1986. The Philosophical Disenfranchisement of Art. Columbia University Press, New York.
Darwin, C. 1871. The descent of man, and selection in relation to sex. John Murray, London.
Endler, J. A., and A. L. Basolo. 1998. Sensory Ecology, Receiver Biases, and Sexual Selection. Trends in Ecology & Evolution 13:415-420.
Lenski, R. 1999. A Distinction Between the Origin and Maintenance of Sex. Journal of Evolutionary Biology 12:1034-1036. -distinguishes between the orgin and maintenance of sexual reproduction
Turney J. (2004). THE ABSTRACT SUBLIME: Life as information waiting to be rewritten. Science as Culture, 13, 89-103. Retrieved July 17, 2008, from
Dissanayake, E.: What Is Art For? Seattle, University of Washington
Press (1988)
Dissanayake, E.: Homo Aestheticus : Where Art Comes from and Why. 1st University of Washington Press ed. Seattle, University of Washington Press (1995)
Healy, S., & Braithwaite, V. (2000). Cognitive ecology: a field of substance? Trends in Ecology & Evolution, 15(1), 22-26.
Bergson, H. (2005). Creative Evolution. Cosimo Classics.
Ryan, M. J., Phelps, S. M., & R, A. S. (2001). How evolutionary history shapes recognition mechanisms. Trends in Cognitive Sciences, 5(4), 143-148. Retrieved July 17, 2008, from http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VH9-42PC695-G&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=7e622bb148b27c4cbf3d290d0a790563
Arak, A., & Enquist, M. (1995). Conflict, Receiver Bias and the Evolution of Signal Form. Philosophical Transactions: Biological Sciences, 349(1330), 337-344.
Endler, J. A., & Basolo, A. L. (1998). Sensory ecology, receiver biases and sexual selection. Trends in Ecology & Evolution, 13(10), 415-420.
Jansson, L., & Enquist, M. (2003). Receiver bias for colourful signals. Animal Behaviour, 66(5), 965-971. Retrieved July 17, 2008, from http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6W9W-49J8TBN-J&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=d7fd52368874c927aac68023a8029efd
Scourfield, J., N. Martin, G. Lewis, and P. McGuffin. 1999. Heritability of social cognitive skills in children and adolescents. Br J Psychiatry 175:559-564.
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.
Steps to a square cocoon.
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.
Ashwathnarayann
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.
Checking the loom's warp.
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.
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.
Fig 1. This image maps the products produced by the United States in 2000. The squares are things they are good at – in the US's case vehicles, chemicals, forest products, for example.
Fig 2. This image maps the products produced by India in 2000. The squares are things they are good at – in India's case textiles, chemicals, and diamonds, for example.
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.
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 mitochondrial sequence used to produce the pattern next to shuttles that carry the silk thread through the warp.
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.
Checking and threading the warp. You can see the silk fibers and how thin a single one is. It takes years to master silk weaving because it is a very delicate and dexterity-rich process.
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.
Finished pattern stretched on the loom.
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
