The CDC’s done a really smart thing. They lied. They created an entirely “unscientific” risk to respond to a completely “scientific” human bias.  The CDC provided an emergency management and disaster preparedness plan in case of a Zombie Apocalypse. This says two things to me: 1) the CDC is serious enough in its priorities to ignore the boundary work that usually goes on in science organizations that tries to keep culture and science separate, and 2) they understand that human bias often impedes our ability to prepare for more “rational” risks.

So I would call this a media coup – especially if (as I suspect) there was a huge spike in visits to their site since the story crashed the server.  I’m sure it helped that some people are actually predicting a zombie apocalypse this weekend.

What I like about this is the acknowledgment that people are interested in fiction at least as much as they are in reality. As a scientist or policy maker in disaster management, it’s worth recognizing that people aren’t going to respond or think a certain way just because it makes the most rational sense.  Zombies may make more sense because they tap into deeper fears and hopes and long-held narratives that are embedded in our cultural fabric.

post-normal science

Using zombies to carry the more important message of preparedness – and the specific steps to take – is way more important than the reality of a zombie apocalypse. Then again, better safe than sorry!

Evolutionary biologists take note!

On Tuesday evening I had the pleasure of meeting up with some fellow UM alums during an information session for the Ross Business School. I didn’t graduate with an MBA; I did my MFA in the School of Art & Design. Nonetheless, I was welcomed and had the opportunity to share my perspectives on what makes Michigan different from other universities and experiences. Actually, I think it is becoming increasingly relevant that students in art and design connect with business students and vice versa.

The highlight of the evening was a lecture by Venkat Ramaswamy, Hallman Fellow of Electronic Business and Professor of Marketing at the Ross School of Business, University of Michigan. During his visit to India he was launching his new book, “The Power of Co-Creation”, and he gave a very nice explanation of co-creation to the audience of prospective MBAs and Alums.

For me, the lecture was especially timely. I have been diving deep into the theory and practice of service design for the last eight months. My goal is to use knowledge of complex systems and dematerialized practices as options for thinking, teaching, and solving problems that can benefit from the engagement of multiple stakeholders. Some of these problems range from the provision of water resources, delivery of health services, discovery of patterns in public health, the maintenance and design of infrastructure, or even how learning is measured and fed back into teaching and course content.

Prof. Ramaswamy’s talk focused on examples that demonstrated co-creation as a paradigm for value creation. He provided a sample of instances where the design of platforms focuses on interactions between enterprise providers (supply chain, enterprise planning, customer relationship) on one hand – and stakeholders on the other. The key part of the value creation lies in the assembly of a platform through which the process of engagement and co-creation can take place. In this way, engagement happens first, enterprise second.

Seoul OASIS co-creation & planning includes the use of images to illustrate the suggestions.

Seoul OASIS co-creation & planning includes the use of images to illustrate the suggestions.

Venkat’s first example came from civic planning in Seoul, South Korea. OASIS is a platform for engagement with public services. It facilitates citizen engagement with the city council using a combination of online, video, and face-to-face platforms. To make it an effective platform, complaints are not allowed – only suggestions. The facilitators also ask/keep the suggestions limited to the goals that have already been determined. So the question civic participants have to ask themselves is, “How do we achieve our goals?”

Civic Participation in Seoul OASIS

Civic Participation in Seoul OASIS

The participation process begin with (1) suggestions which get tagged by the participants. The tags allow people to start structured (2) discussions of the ideas. About 12% then get taken for (3) off-line examinations. Eventually there are (4) Seoul OASIS meetings which are filmed live and where stakeholders and civic service providers get to interact. Finally, a handful of suggestions make it to (5) implementation where the project gets documented along with benchmarks and other accountability checks.

Delhi-Traffic-Police-get-social

Caja Navarra (Spain) is pioneering civic banking using engagement platforms to make an impact in the social sector. It shows customers how much it makes from their savings and provides them with the ability to choose from an array of eight or so recipients of their social contributions. The recipient organizations are further pushed to present how they use the money as a result of the participation. The benefits also feed back to the bank’s ability to attract new customers. By providing “gift cards” with preset amounts, new participants can log on and get involved with their donations. Meanwhile, the bank is then able to show potential customers how their money would be used by Caja Navarra as opposed to the customer’s current bank.

The Gameful Leaderboard

The Gameful Leaderboard

All of this reminded me of some other platforms that tie emerging enterprises with potential stakeholders. Kickstarter is a new platform for ideas that need capital to get their projects off the ground. Anyone can contribute, and it only depends on the project’s ability to pitch their idea – and maybe some well-placed social capital (here’s some tips on managing a kickstarter project). One hugely successful project pitch that was launched is Gameful (exceeding their funding goal by over 3000%). It’s an online Secret HQ for gamers and game developers who want to help change the world and make our real lives better. The project’s developers did a really nice thing in pitching the project. They set of levels of giving, that mimicked some game tropes like secret entry points and awards.

Co-creation and service design are largely about the engagement that happens in the development of product and service offerings. Later as we ate dinner, I asked Prof. Ramaswamy what it might mean to go beyond products and services. What would happen, for example, if co-creation impacted the evolution of the core business model and plan? Eric Beinhocker explores some of the conditions for how this might happen in his book, The Origin of Wealth. One of the central themes of the book revolves around how businesses themselves are a form of design. The design of businesses encompasses how to understand the market and connected institutions, product and service offerings, operations, marketing and sales, strategy, and the organization itself. If, as Beinhocker argues, business designs evolve over time through differentiation, selection, and amplification, then it stands to reason that co-creative platforms for engagement can distribute that work as well as just the product and service offerings. The only question is where will it happen?

A Simple Model of Attachment

The image above was the first draft. This is the second. Thanks to Aliya for good, perceptive comments.

attachmentModel_v2

Premises:

Culture as the processes that allow the uptake of processes, procedures, information, beliefs, values and social norms.

Cultural affiliations are attachments.

Attachments and reattachments are limited (quantity) and constrained (quality) by pressures.

Aspiration is a cultural step in creating capability.

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.

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.

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

Good find..thanks to Zack.

Cognitive Biases – A Visual Study Guide

I’ve been casually reading Scott Atran and Douglas Medin’s The Native Mind and the Cultural Construction of Nature since I came back from the U.S. in January.  I picked the book up for a few reasons. One, I was familiar with Scott Atran’s work after running across it while I was studying at the University of Michigan.  Atran is an anthropologist who has been working to integrate psychology and anthropology in pursuit of a better perspective on how the natural environment and the social landscape interacts to affect belief, behavior, and practice.  Two, I am interested in how cognition facilitates learning and behavior, especially in a shared resources or public infrastructure context.  Some of Atran’s more recent work deals with negotiations and intercultural understanding for problems ranging from terrorism, common resources, and Iran’s nuclear policy.  Third, the discussions and research in the book can be helpful for artists, designers, teachers, and evolutionary biologists who want to gain better control or understanding of how, effectively, epistemology develops.

I found one particular passage to be quite helpful for a project I am working on at the moment. It deals with relevance drawing from Sperber and Wilson’s book on communication and cognition. Relevance is a pretty subjective measure of how much something matters to someone.  The articulation of relevance in these pages shows ghosts of Bateson’s difference that makes a difference, but here there is an efforts to start to describe exactly what aspects of cognition make something relevant–that is, how does the environment and one’s interactions in it affect meaning?  pay attention teachers…this is where it gets relevant to learning.

Here’s some notes:

Relevance: if processing an input at a certain time yields cognitive effects.

Cognitive Effects =

1. revision of previous beliefs
2. derivation of contextual conclusions following from input taken together with previously available information

So:

greater cognitive effect = greater relevance

While:

greater effort = lower relevance

Thus:

Salient information has greater relevance given the lower effort it requires.  Atran and Medin make this point be describing their research with different groups’ interpretations (interpretations = mappings from objects, situations, problems, and events to words. In an interpretation, one word can mean many objects) of ecological relationships and taxonomy.  They also studied school children who had a more nuanced view of ecology and compared them to urban children to try to help understand why they had different experiences in the classroom.  The conclusions supported the idea that textbooks and instruction was not relevant enough to support the expansion of learning among those with more nuanced perspectives (perspectives = mappings from reality to an internal language such that each distinct object, situation, problem, or event gets mapped to a unique word).

Learning, then, is guided by what is already known. What is learned first often becomes a category ideal.  It’s like when your idea of what tastes good, what a certain kind of flower is, or how to do a task is based on what you first learn. It’s also affects things like what we think of when we think of a bear. My image of a bear may be based on North American species like the black bear or grizzly. In India, an image of a bear may be based on their Himalayan relatives.

This seems to resonate somewhat with patterns of cognitive bias studied across different organisms in evolutionary biology in an attempt to get a better understanding of sexual selection.  Cognitive or sensory bias, as studied in evolutionary biology, refers to an organism’s set of preferences.  It’s similar to judgment biases studied by psychologists and micro economists (e.g. Tversky and Kahneman). However, in biological terms, sensory bias often has a genetic/sensory basis and can significantly affect mating and reproduction. Some well-studied examples include how Tungara frogs (Ryan lab at UTexas) or even crickets (Zuk lab at UC Riverside) influence mate choice with different call structures or signals (e.g. deep, red, loud, frequent, etc).

So in an experimental, teaching, or design setting, good examples of categories are ones that are familiar, have a high word frequency (use = familiarity + context), or that represent ideals.  So as we design interfaces, software, interactions, and signs for access, it makes sense to consider categories that are culturally relevant and that have legacies of use in context.  Additional learning uses these categories as supports (scaffolds?) to build on.

This is why representation of goals and categories is so important.  The implicit organization of knowledge around goals creates category ideals, subsequently driving category based inference–that is, the creation of new knowledge from what already exists.

So in terms of deriving an experimental practice from these ideas, a student at CEMA, Aliya, has been trying to look at how naming objects as concepts (decategorization?) rather than the names they have been given.  Thus a “chair” becomes a “people holder” or a “step ladder” depending on new contexts of use.  It leads to the question, “How do we take objects from everyday life & create a stimulus that provides an opportunity for reflection & engagement on the use, interaction, and consumption that the object supports—all while waiting for whatever that object does?”

There is a critical issue at the core of the discussions about innovation that isn’t being discussed. I hesitate to say that it’s the elephant in the room, but when in India…

This issue is encapsulated in an exchange I had with Gregg Davis during the questions period following his presentation at the “Leadership through Design Summit” in Bangalore just before the end of the year. In reality it was only a question and response, but it’s worth sharing.

Davis presented a talk during the IDEAS section on “Innovation for Business Transformation.” The presentation was titled something like “Brand, Design and The Brain: A New Methodology for Building Design and Brand Attributes Based on Recent Scientific Studies of the Brain.” I grabbed this title from a recent talk he gave at the CONNECTING’07 Congress of Industrial Designers, but it was basically the same thing.

What is interesting about this topic is how cognition studies are being used to inform business and communication practices aimed at better attracting customers. The talk itself was fascinating and full of insight and ideas from cognitive science. Davis presented Magnetic Resonance Imaging (MRI) imaging as a tool to more closely predict how consumers make choices. MRIs “take a picture” of the brain basically by showing where blood flow is most intense. MRIs can even be performed over a time-interval to show how changes in blood flow happen over time. The basic idea is that blood flow increases to parts of the brain that are being used most intensely. So if you are having an emotional response to something, then the amygdala may light up. If you are recognizing metaphors, then the angular gyrus may be involved. If you are involved in reasoning or planning, then the frontal lobe may show a signal (and so on). When Davis and collaborators presented people with “familiar products” they observed that regions associated with comfort lit up. When they showed “unfamiliar products” the regions associated with anxiety lit up.

Here is where it gets really interesting. Davis suggested that one of the purposes of this approach was to do many of the things that artists do. He put it another way by saying that there is an assumption that artists typically “unlock” those regions of the brain associated with emotion that also, incidentally, affect non-rational consumer choice. Given that marketers and business folks are interested in understanding (and in fact controlling) choice, it’s not surprising that they would be interested in those factors and patterns in the brain that affect consumer decision-making.

Okay, let’s assume that artists do indeed capitalize on those “emotional” and “non-rational” regions of the brain (I think it’s reasonable). Why then do we need to expend the vast resources and put people under the enormous imposition of MRI technologies in order to do things that are already possible if you involve artists in the business and design processes? The response I received was great. Davis started by commenting that this was a hugely dangerous question and that it got to some of the issues of the relationship of business and artists. He didn’t go much further than that, and it was fine by me. I could understand why he wouldn’t.

This is the key question. Why do we do things that are technically feasible but ultimately more harmful and manipulative to individuals? Why are big problems approached from a technological perspective rather than the more complicated social one? I understand why this problem exists; artists talk back and make suggestions that businesses do not want to hear. MRI machines just do what they were made to do. While there is some interesting data and observations that can be made, where will the real design innovation come from? Will it come from the more precise matching of people’s preferences to product offerings, or will it come from people who have the ability to predict and enliven design ecologies to respond to the cognitive changes that cause people’s preferences to shift and flux in an ever-changing environment?