Danger! May change your views!

Came across this in an issue of IEEE Computer today. It’s a simple conceptual model from the 1960s by a guy called Bruce Tuckman of the stages small groups go through; groups such as committees, work groups, and project teams. The basic stages seem obvious, but, as with many models of human behaviour, the value comes from their being made explicit such that they can be recognized, acknowledged, and facilitated appropriately.

Here’s what the original article says (my emphasis):

Groups initially concern themselves with orientation accomplished primarily through testing. Such testing serves to identify the boundaries of both interpersonal and task behaviors. Coincident with testing in the interpersonal realm is the establishment of dependency relationships with leaders, other group members, or pre‑existing standards. It may be said that orientation, testing and dependence constitute the group process of forming.

The second point in the sequence is characterized by conflict and polarization around interpersonal issues, with concomitant emotional responding in the task sphere. These behaviors serve as resistance to group influence and task requirements and may be labeled as storming.

Resistance is overcome in the third stage in which in-group feeling and cohesiveness develop, new standards evolve, and new roles are adopted. In the task realm, intimate, personal opinions are expressed. Thus, we have the stage of norming.

Finally, the group attains the fourth and final stage in which interpersonal structure becomes the tool of task activities. Roles become flexible and functional, and group energy is channeled into the task. Structural issues have been resolved, and structure can now become supportive of task performance. This stage can be labeled as performing.

So, basically, small groups go through the following phases:

• Forming – the group forms, and members break the ice and begin to get to know one another. Individuals develop the confidence necessary to function within the group.
• Storming – group members begin to assert themselves through conflict as social roles are negotiated. They act to maintain their own individuality as well as determine their status within the group.
• Norming – common vocabulary, assumptions, and goals are articulated, and the group begins to function together. Group identity forms.
• Performing – the group becomes productive, trust becomes firmly established, and group energy is channeled primarily into the task at hand.

I don’t know about you, but these stages certainly feel familiar. I don’t think it’s useful to claim that these are distinct and clear stages, however. Rather, I think they’re best thought of as overlapping phases describing a ‘natural’ progression. With that in mind, then, here’s a bunch of ways you could use this model:

Firstly, with a model at hand, it’s easy to see when behaviour deviates from a ‘normal’ pattern. This isn’t intrinsically bad, but, if unexplained, may be indicative of certain problems within a group.

Secondly, individuals and subgroups might not necessarily move through this progression at a uniform rate – if part of the group is still stuck storming, it makes it hard for the rest of the group to begin norming. In such situtations, a skilled group leader might be able to gently nudge such individuals by, for example, allowing them other outlets to express themselves.

Thirdly, it seems like these stages aren’t just what normally occurs, but also what needs to occur for a group to function. It’s probably important to be aware of this when forming expectations of a group’s performance.

Fourthly, it’s always nice to have a vocabulary to describe things like this, particular given that the elements of group behaviour are normally quite implicit.

Edit: Lastly, it’s interesting to think about the emotional conflicts and outbursts that sometimes occur and realize that they’re actually just part of the process rather than some intrinsically negative distraction.

While brain stoming with Julian about the implications of ‘always-on augmented reality’* on human communication, I decided I needed a way of collaboratively drawing mind maps so that we’d have some sort of concrete record of the ideas coming out of our conversation. Normally, I make notes of my own, but they’re hard to share meaningfully – what I really want is a shared whiteboard of some sort.

I’d not looked at networked collaboration tools in a while, and googling, I was pleased to find MindMeister, a collaborative mind-mapping that suited my needs well. It has the following nice features:

• Copes with multiple people editing a map simultaneously
• Supports really big maps fairly well
• Has lots of nice notification tools for maps that are likely to be tweaked and developed over time
• Is free, at least for the functionality I required.

Anyway, if you’re collaborating with people over distance and you’re doing creative work, this might be worth a look to you.

Got any other cool collaboration tools you’re using? Let me know!

* I’ll write more about this later – I’m trying to differentiate my thinking from the burgeoning mass of AR demos involving a webcam, ARToolkit, and a bunch of markers. AR has so much more potential than these suggest, and I find them constraining.

I recently listened to this interview with Denis Dutton on Bloggingheads.tv about his new book “The Art Instinct“.

I haven’t read the book, so I can’t comment on it in detail, but I can definitely tell I want to read it. The basic premise is that the art can be explained in evolutionary terms as the emergent result of a series of adaptations supported partly by natural selection, and partly by sexual selection.

Since the borders of art are often fuzzy, he begins by establishing a working ‘cluster definition’ consisting of 12 characteristics, where anything that matches all characteristics is definitely art and anything that has none is definitely not.

Works of art:

• give us direct pleasure
• are stylistic
• have expressive individuality, a mind behind them
• are creative and novel
• have a surrounding air of criticism
• challenge us intellectually
• tend to exist within institutions and traditions
• are representative of reality
• embody skill and virtuousity
• are a focus of attention
• are emotionally saturated
• exist partly in our imagination

The rest of the book applies the two elements of Darwin’s theories, natural selection and sexual selection, to art. Natural selection is what we most commonly understand as evolution, and has to do with adaptations that make us more likely to pass on our genes; an art related example is the ability to construct imaginary situations and communicate them that assists in both planning and survival. One example cited is an art experiment in which people from a wide variety of countries were polled as to their tastes in calendar pictures. These were then painted and compared, with interesting similarities – see the book Painting by numbers for an account of this.

Sexual selection, on the other hand, is poorly understood by most people, if known at all. Where natural selection deals with adaptations that help individuals survive within their environment, sexual selection deals with adaptations that help individuals compete with others of their species for mates, even at the cost of reducing their survivability. It explains peacocks tails, the antlers of reindeer and the combative mating rituals that go with them, and behaviours such as infanticide among lions. In the context of art, sexual selection ties directly into, for example, displays of skill and virtuosity.

I’m fairly certain that this will be contentious with some; it builds on evolutionary psychology, itself controversial, and undermines relativist theories of art. As I’ve mentioned above, I’ve not actually read the book, so I can’t argue it in more detail, but I find this sort of explanation a lot more accessible and plausible than relativist theories, though I’m open to these being a contributing factor layered on top. Of course, I’m an engineer, so I guess the relativist argument would be that I have a pro-science bias and relativism still holds.

Stepping back, though, we humans share a whole range of common physical attributes, all explained by evolutionary processes, and it’s absurd to think that these don’t play some role in our appreciation of art. Furthermore, neurological pathologies reveal that slight variations or damage to the physical structure of our brains results in perceptual and behavioural differences far in excess of those that exist between cultures, which suggests to me that evolutionary processes not only play some role, but play a major role in explaining art and its appreciation.

Either way, this book sounds like a fascinating read and a great starting point for discussion.

Why do I blog this?
Recently, I’ve written about music appreciation and explained taste as a set of priorities for the various attributes by which we judge a given performance. Assuming the book’s arguments hold, music, being just another form of art, can be explained in evolutionary terms. Is it reasonable to expect that music appreciation is also explainable through evolutionary principles? To what extent are our tastes defined by our genes, as opposed to our environments or simple variation?

It’s kinda awesome to see and hear people from Canterbury being interviewed like this – it’s nice to be reminded that despite New Zealand being way down at the bottom of the world, we’ve still got some great minds in our universities.

Edit: I was going to point at this article by Dutton “Aesthetics and Evolutionary Psychology” from a few years back that fleshes some of these arguments out a bit..

Following on from last week’s post on wicked problems, I got to thinking about the general sorts of challenges that occur in design, whether it be design of software, bridges, posters, web sites, or even social policy.

It seems there’s three main types of challenge:

• Problem definition – exploring a problem space, identifying a particular problem within that space, then defining it with constraints, goal states, and resource availability
• Creativity – coming up with ideas and strategies for solving a problem
• Negotiation – making decisions as a group; examples include deciding which strategies to employ, which graphics to use, and how resources should be expended.

In addition, there are many tasks that are auxiliary in that they support the design activity and provide feedback to it, but aren’t really part of it. For example, in software engineering, implementation isn’t really part of the design activity it supports, though it may provide feedback, perhaps in the form of technical constraints. Similarly, administration and project management provide feedback concerning resource availability, team morale, and so on, but they’re not really design.

You might have noticed that I’m conflating the activities of design and problem solving. While they’re not equivalent, they’re very closely related. Depending on your background, you might think of problem solving as being the stage in the design process that follows problem definition and precedes implementation – in this case, you might think of problem solving as the process of creatively deriving a solution to a design problem. On the other hand, you might see design as a stage within problem solving in which one designs potential solutions before trying them. I don’t think it’s necessary to get any deeper into this – it’s just interesting to note that you can frame design as problem solving and problem solving as design.

Another interesting thing to note is that the auxiliary tasks, while not design themselves, incorporate lots of problems to which one designs solutions, and that these can be considered whole design problems not necessarily related or subordinate to the larger design activity in which they take place. In this sense, you can think of design as a hierarchical composites of smaller designs. If you take the broader understanding of design as problem solving, you can apply this observation to a vast range of activities.

I’m simplifying things here – I don’t want to imply that design consists of three clearly defined tasks based around the challenges above and supported by various auxiliary tasks. Nor do I want to suggest that any of these tasks can or should be performed in isolation (particularly in the case of wicked problems). Rather, the tasks that compose the design activity are tightly interwoven. By drawing out the challenges above, I only want to bring attention to them as things that are hard and that require particular skills to address.

I’m tempted to argue that, taken together, the skills required for each of these three tasks form a set of skills that are common to designers across all the various fields of design, from software to graphics, from architecture to social policy. But I’m not sure how I’d substantiate that, so I’ll it as a speculation.

What challenges am I missing? What other skills are common to all designers, do you think?

Today I read a paper from 1971 by Horst Rittel and Melvin Webber about “wicked problems” – problems that are intrinsically difficult or impossible to solve in the sense that one can solve a crossword or mathematical proof, or win a game of chess. Wicked problems abound in policy questions and design, and it’s interesting to think about what differentiates them from these other “tame problems”.

The paper defines a wicked problem as one with most of the characteristics in the list below. Bear with me, because being able to spot a wicked problem and thus infer the consequences of that fact is quite a powerful tool for thinking about decision making in pretty much any context. Once you’ve got a clear idea of the concept, you can start seeing them everywhere – in policy such as city planning, in international conflict, project management, personal time management, and even in family Christmases. They’re everywhere, and, unlike tame problems, they’re impossible to solve absolutely, though sometimes they can be resolved partially with relative ease.

1. Wicked problems can’t be definitively formulated. To formulate the problem, we must make decisions about how to conceptualize it, limiting our consideration in some way. Different people will conceptualize the problem differently, and these differences might not be readily apparent. Furthermore, a wicked problem may be partially obscured such that we cannot see its full extent without attempting a solution.
2. We can’t tell if we’ve completely solved a wicked problem or not. Wicked problems are usually qualitative rather than quantitative and often have fuzzy boundaries. Though we can arbitrarily declare stopping conditions, these conditions are, well, arbitrary.
3. We can’t easily rank possible solutions to a wicked problem. Different stakeholders want different things for value-oriented and self-specific reasons. Points 1 and 4 also suggest that the problem will only be known incompletely and there is significant uncertainty about the effects of each solution.
4. We can never assess the full effects of a solution. We don’t know for sure in advance whether our solutions will have the desired effect and, after implementing them, its impossible to predict or observe their full consequences over time due to ripple effects.
5. Solutions are single-shot. We only get one chance to solve the problem because our solution always changes it, affecting any future solution attempts. This can often make it hard to learn from mistakes.
6. The solution set is unbounded. We can’t list or exhaustively describe the possible solutions, so we never know whether we’ve got the best solution (even assuming we can rank the solutions them).
7. Every wicked problem is essentially unique. Though two problems may appear fundamentally similar, they are of sufficient complexity such that it is impossible to determine whether apparently minor differences will have any effect. Two cities may be laid out almost identically, but this is not enough for us to conclude that public transport should be laid out exactly the same way.
8. Wicked problems are linked to other wicked problems. Almost every wicked problem can be thought of as part of another wicked problem and as having several wicked problems as part of it. Inevitably, this leads to cycles of problems that cannot be solved independently. Crime leads to jail time, which leads to abandoned children, which leads to crime.
9. The effects of a wicked problem and its solutions can be explained in many ways. Not only is it hard to formulate the problem and evaluate its possible solutions, it’s very easy, after the fact, to explain its effects in a variety of ways. For example, FDR’s New Deal and WWII spending is hailed by Keynesian economists and left leaning politicians as having ended the Great Depression, while these same policies are damned by market economists as having prolonged its effects. The problem and its solution can be suborned by multiple conflicting narratives, again making it hard to learn from our actions.
10. The costs of being wrong are often high. Losing a game of chess doesn’t hurt a lot, but arranging a family get together that goes wrong can be painful. Bad solutions may create problems whose size exceeds the original problem. Given that wicked problems are often policy related, they can adversely affect people’s lives.

This list constitutes a polythetic or cluster definition; that is, problems must have some, but not all of the criteria to be considered wicked. Furthermore, problems possess them to a greater or lesser extent than others, implying the idea of a continuum of problem wickedness. Polythetic definitions are normally used to define complex concepts in philosophy, and the fact that such a definition is required to define wicked problems suggests that they are not a clear or natural category as the paper suggests.

That said, however, the category of tame problems is much clearer. It consists of problems with stopping criteria, clear correctness of outcomes, limited solution action sets with clear results. It seems, then, that wicked problems are perhaps best understood as the set of all problems that are not tame.

One implication of the wickedness continuum is that wicked problems could be made less wicked if we understood the factors that make them wicked. Unfortunately, however, the list of criteria above is primarily descriptive, not explanatory, and so only of use as a starting point. On Tuesday, I’ll be participating in a further discussion on this topic in which I’d like to explore explanations of what makes a problem wicked. This would, I think, give a better definition as well as some ideas for how wickedness might be reduced. Below are some candidate explanations:

• Problem complexity and chaos; in particular the existence of emergent properties and behaviour
• Situation of the problem within an unbounded or open system
• Tight coupling and / or dependence of the components of the problem
• Visibility or availability of information within the problem context
• Conflicting definition or identification of the problem

There’s one last point I want to make. Being written in 1973, the paper gives the impression that the difference between wicked and tame problems maps fairly clearly to the difference between abstract, mathematical or game problems and real political and social problems. It’s interesting to note that in recent years, the term wicked problem has been used to describe problems in software engineering and design that exhibit many of the same properties of the social problems outlined in the paper, demonstrating that it is not abstraction itself that makes a problem tame. It would also be interesting, I think, to look at some of the strategies employed by engineering teams to deal with wicked software problems, and work out if they could be applied to wicked problems in a social or political context. Food for thought, anyway.