Rittel and Webber’s 1973 formulation of wicked problems in social policy planning specified ten characteristics:
There is no definitive formulation of a wicked problem (defining wicked problems is itself a wicked problem).
Wicked problems have no stopping rule.
Solutions to wicked problems are not true-or-false, but better or worse.
There is no immediate and no ultimate test of a solution to a wicked problem.
Every solution to a wicked problem is a “one-shot operation”; because there is no opportunity to learn by trial and error, every attempt counts significantly.
Wicked problems do not have an enumerable (or an exhaustively describable) set of potential solutions, nor is there a well-described set of permissible operations that may be incorporated into the plan.
Every wicked problem is essentially unique.
Every wicked problem can be considered to be a symptom of another problem.
The existence of a discrepancy representing a wicked problem can be explained in numerous ways. The choice of explanation determines the nature of the problem’s resolution.
The planner has no right to be wrong (planners are liable for the consequences of the actions they generate).
Conklin later generalized the concept of problem wickedness to areas other than planning and policy. The defining characteristics are:
The problem is not understood until after the formulation of a solution.
Wicked problems have no stopping rule.
Solutions to wicked problems are not right or wrong.
Every wicked problem is essentially novel and unique.
Every solution to a wicked problem is a ‘one shot operation.’
Wicked problems have no given alternative solutions.
From Grub-Street Journal, October 30, 1732: the “art and mystery” of printing in the “literatory” of publisher Edmund Curll
think of Grub St as 18c ‘Private Eye’ »> french revolution 1789-1799 & the Great Reform Act of 1832 »> representative government instead of authoritarianism »> discussion, creation & extension of civil rights
And while [the philosophes] grew fat in Voltaire’s church, the revolutionary spirit passed to the lean and hungry men of Grub Street, to the cultural pariahs who, through poverty and humiliation, produced the Jacobinical version of Rousseauism. The crude pamphleteering of Grub Street was revolutionary in feeling as well as in message. It expressed the passion of men who hated the Old Regime in their guts, who ached with hatred of it. It was from such visceral hatred, not from the refined abstractions of the contented cultural elite, that the extreme Jacobin revolution found its authentic voice
the great unwashed »> the great uncalibrated; all part of the data collection process, no matter how messy; not simply acting as subjects of the measurements or passive receivers of the wisdom contained therein
processes of measurement »> construct understandings of our environment »> build up intuitions about how we may affect it »> question the standards of evidence of others.
impartiality, freedom from ethical decisions (‘it’s not me it’s in the data’)
- the spectacularisation of data, revelling in complexity only so that ‘experts’ can rescue us from the cacophony: scientists, urban planners, yes, even artists
- the concerning thing about this neo-postivism is when it’s applied to the design and manipulation of our cities because these processes have their own ‘god fantasies’:
efficiency (those big biz initiatives that use “Smart” throughout their PR material)
all the things that go counter to the sustainability of what makes a city a city
social goals that rarely have anything to do with technology and sound suspiciously like the sorts of things urban planners were saying in the 50s and 60s when they gave us highways and highrises/tower blocks
- The alternative is to look not at the data, but at the people that are deciding to create the data and the processes they’re using — not “making data public” but the public making data.
- think of a learning/teaching paradigm (rather than knowledge); activity rather that state
- crafting data means going through those same processes that so-called ‘scientists’ go through
identifying patterns & outliers
understanding dynamic range
importance of context
standards of evidence, particularly important in climate change
- discovering and sharing:
dealing with heterogeneity
- take a step beyond, people as sensors (engines for computing complexity), not quantified self but quantified and qualified selves, instrumenting the world to give it a voice.
- embrace the complexity
- i don’t believe we can deal with the world’s challenges through reductivist solutions extracted from an oracle-like data-pool (it was probably reductivism that created the problems in the first place).
- the issues and challenges of our world, which have indefinite parameters, demand creative propositions that are probably so complex that they require:
cooperation between people who don’t agree with one another, who have no consensus — but collaboration doesn’t need consensus
people who have understandings they cannot explain to each other,
insights that are necessarily so intricate that they will take more than one person to tell the stories
finally got a chance to see your momo talk which, needless to say, was GREAT.
the discomforts of occularcentricity have preoccupied me since my first days in architecture school where it was all about the ‘drawing’ (there was even a conference on the theme…) so your discussion resonates well. there was much food for thought, and i realise you’ve probably had thousands of comments back to you, so let this simply settle down somewhere in the cacophony.
there was something missing that i think would add even more weight to your argument and i wanted to throw it out there for future conversation. i think it’s problematic, for the construction of your thesis, to counter the “it’s all in the eyes” perspective with its polar opposite “it’s all in the brain” because most visual research of the past few decades shows that it’s actually a little of both.
what these papers outline is a perceptual framework that is constructive. you could say it’s founded on a conversation between eyes (or other sensors) and brain, not simply a one-way transmission; so it’s not that the we paint the world before us, or that we are merely passive receptors of neutral information coming towards us, but that we are active participants in the construction of our perceptions. it is, if you will, the cacophony of multiple sensors (and histories thereof) that is required for such perception.
AR, as we’re collectively starting to call it, appears to confuse what it means to perceive reality. to me, it makes the mistake of assuming there’s a difference between “real” reality, and the so-called “virtual” portion that’s overlaid on top of it. the only “augmented” reality, is the one that’s constantly being built up through our interactions through the world — whether that’s through a mobile phone, through our sunglasses, or even just through closed eyelids.
the process of understanding, it seems to me, is a process of constructing an understanding. the problem with AR, then, is that it assumes that the reality “out there” is fixed, and that we’re merely passive observers that need some kind of markup on it to help understand it “better”. it’s like the terminator analogy you cited: AR is set up so that “we” are sitting inside, simply waiting for info to come in (like arnie “seeing” inside his own head with its own reductio ad absurdam) and all the concomitant repercussions on what this means for our own agency (or lack thereof) in the world. it also assumes that we all see the same thing, which we manifestly do not — and this isn’t because of some distortion in our perceptors (which AR appears to seek to correct for) but because we each have our own constructive processes, founded on our own heterogeneous perceptual frameworks.
this is really important for our individual and collective relationships to our cities: because AR as it’s usually framed diminishes the fact that our cities are constructed, every day, with every conversation we have, every space we inhabit, every structure we erect, and every step we take through them, by us all together. cities aren’t simply entities that we occupy and need guidance through. von foerster’s paper goes into possible consequences from this this on the way we relate to each other.
the other part of your conclusion - the fact that seeking to understand through visual perception alone does reality a disservice - is also supported by this line of thinking. since perception is a constructive process it necessarily is affected by everything we experience, from the things that we hear to the way we are moving, so building solely for vision is an incredibly restrictive funnel on understanding.
same conclusion, slight different founding blocks.
whoops, this ended up a lot longer than i’d planned it to. maybe we can pick it up in london…
Cities are heterogeneous, that’s why we like them. People always find ways to distinguish themselves from each other - this is the cause of both conflict and creativity. Clearly, technology, which is a manifestation (and delimiter) of social relations, will affect the way we construct our cities. Ubicomp/pervasive computing/situated technologies/or whatever you want to call ‘em offer both citizen-led sense-making and authoritarian control structures. So the question is not “will things change?”, nor “how will they change?” but “how do we want them to change?”…
- i *think* the side i’m supposed to be taking, because i’m CEO of Connected Environments Ltd and founder of Pachube.com, which provides an open API for data connectivity, that collects and connects data from sensors, energy meters, weather stations, building management systems, air quality monitors — almost anything that produces data around the world. i *think* i’m supposed to be espousing the view that open data is going to lead to more wonderful, fuller and productive, sustainable lives/environments/cities.
- but that’s a misunderstanding of what pachube is all about; and i’m concerned that the motion is misleading
- so the first thing i’m going to do is reject the boundaries of the motion entirely - to frame the conversation in terms of asking whether open data will change how we interact with our cities is problematic on so many levels.
- first: of COURSE technology changes our relationship to our cities. cities accrete technology, and technology is a manifestation (and definer) of social relationships.
- second: the question (‘will xyz change the way we interact with cities?’) contains within it the idea that our cities are abstract entities separate from ourselves… that we can somehow “interact” with cities as things that are separate from ourselves. on the contrary: cities ARE interaction, or cities are the accretions of interactions, they are not some static solidified entity that we, as consumers, simply ‘interact’ with. we create, and recreate our cities with every step we take, every conversation we have, every nod to a neighbour, every space we inhabit, every structure we erect.
- of course, what we consider ‘conversations’, ‘neighbours’, ‘spaces’ and ‘structures’ have and will continue to change.
- the question is not ‘will things change’ but ‘how do we want them to change?’
- opening up data, which is very much the rage right now, is certainly a useful process, in that it enables a level of accountability that was not previously evident.
- but it’s worth bearing in mind that, simply opening up data is not enough. when a government organisation ‘opens’ up its data, laudable as it is, should not detract from closer inspection. we have to question how and why they opened up that data - is it because it’s non-threatening? how was it compiled or measured? what was the dynamic range? what data was left out? how might it have been used to obscure something else? in essence: how was the data created?
- opening up data can be considered itself a control structure — a means of saying you’re doing something without doing anything at all; while continuing to justify mass privacy invasions of data created by and belonging to citizens.
- the real question, it seems to me is not about making data public, but about finding ways for the PUBLIC TO MAKE DATA.
- how do we all, citizens, contribute to the data collection process? how do we learn and understand our environments through the data that we create (or craft) – because data collection is at its heart a craft? how can it help us question the standards of evidence - evidence that we are asked to believe and comply with by authority figures, by politicians, scientists, media figures, religious figures. (this isn’t to say that what they say is wrong: it’s to say that it’s more important to convince ourselves; that way we understand much better and we can also be part of the process of improving things).
- which is why i launched pachube: it’s not so simply about making data ‘open’ it’s about developing a platform that makes it as easy as possible for everyone — citizens, organisations, companies and city managers alike — to produce, aggregate, share and compare environmental, sensor, energy and any other sort of arbitrary data you want, generated by devices, buildings, energy meters or even virtual environments. it’s about data crafting – a platform that works for players small & large.
- what is worrying right now is the asymmetricality of the conversations between all these entities — and its Pachube’s task to encourage and make possible greater horizontality.
- the ‘internet of things’ is coming - it’s clear we’re going to be inundated with cities replete with sensor networks and all sorts of weird, wonderful and worrying data systems — but what concerns me most is how we, all, can be part of the process of defining what that data is, how it is collected and what is done with it.
- So again, the question is not “will things change?” but “how do we want them to change?”
This book gives a state-of-the-art overview of modeling growth and form of marine sessile organisms - such as stromatolites, algae, and metazoans including stony corals, hydrocorals, octocorals, and sponges -, using large-scale computing techniques, scientific visualization, methods for analyzing 2D and 3D forms, and particle-based modeling techniques. It originates from the workshop on Modeling Growth and Form of Marine Sessile Organisms, held at the National Center for Ecological Analysis and Synthesis, Santa Barbara, California, August 1999. Experts from various disciplines including developmental biology, ecology, computer science, physics and mathematics, who have research interests in modeling the development of these organisms have been invited to contribute.
The book describes all the steps required to develop and experimentally validate morphological models including collecting biological information and methods for specifying and comparing forms. Examples are given of how models are currently being applied to simulate growth and form of marine sessile organisms. Potential applications of growth models and morphological analyses in modern and paleo-bio-monitoring, the detection of environmental change, and the conservation and restoration of marine ecosystems and aquaculture are addressed. The combination of simulation models with laboratory and field experiments provides a powerful tool to obtain insights on how the growth forms of marine organisms emerge from physical, genetic and environmental influences.
An intelligent system is more sophisticated and interprets the property changes of the smart polymer. For example, it consists of a material that emits a stimulus and of an adaptive material that controls the adaptations of one of its properties to counterbalance the stimulus and to maintain a stable state of the system. More precisely, for example, a structure that vibrates in a given phase of operation is associated with a responsive polymer. A vibration sensor informs a generator of stimulus that requests the responsive polymer to modify the mechanical properties of the unit and to counterbalance the vibratory state.
the saturation of the air with moisture, which produces the visible mist effect, depends on a number of physical environmental conditions. these parameters include the speed and direction of the wind, the environmental temperature and the atmospheric humidity.