6.4. A Walk in the Park
As we build our Internet of objects, the permutations of sociosemantic metadata will create new avenues of findability. Where has this object been? Which objects were in close proximity to this object? Who touched my object? Where are they now? The era of ambient findability will overflow with metadata, as every object and location sprouts tags: social and semantic, embedded and unembedded, controlled and uncontrollable.
Imagine the sensory overload of a walk in the park. Every path shimmers with the flow of humanity. Every person drips with the scent of information: experience, opinion, karma, contacts. Every tree has a story: taxonomies and ontologies form bright lattices of logic. Desire lines flicker with unthinkable complexity in this consensual hallucination of space and non-space, a delicious yet overwhelming sociosemantic experience.
How will we make sense of this tower of babble? In the midst of this cacophony, to whom will we listen? Who will we trust? Will we rely on formal hierarchy or free tagging, library or marketplace, cathedral or bazaar? Will we place our confidence in words or people? And are we talking about cyberspace or ubicomp? The answer lies in the question, for we will not be bound by the false dichotomy of Aristotelian logic. To manage complexity, we must embrace faceted classification, polyhierarchy, pluralistic aboutness, and pace layering. And to succeed, we must collaborate across categories, using boundary objects to negotiate, translate, and forge shared understanding.
Of course, even with all this sociosemantic cooperation, the road ahead is long and winding, with many paths not taken. Our ability to make informed decisions will depend on how we allocate attention and trust, how we define authority, and how we employ metaphor. As Alfred Korzybski, the polymathic founder of general semantics, taught us "man's achievements rest upon the use of symbols" and yet "the map is not the territory." We would do well to recall his words and meaning as we take our walk in the park.
Chapter 7. Inspired Decisions
I remember the summer of 1989. I was 19 years old, a sophomore biology major at Tufts University, and a transient in the home of my parents. My passions were, in no particular order, soccer, girls, literature, beer, and artificial intelligence. My summer began in the environmental lab of the Millstone nuclear power plant, where I measured the impact of thermal discharge on marine biodiversity. By day, I studied sand under a microscope, and by night, the works of Dostoevsky, Turing, Hofstadter, and Dennett.
That August, we took a family vacation to France and England. I left the sand behind, but the self-reflections of The Mind's I and the eternal golden braids of Gödel, Escher, Bach traveled with us. In fact, one of my fondest memories is of wandering with my brother through strange loops and tangled hierarchies, surrounded by the rolling green hills of the English countryside. Thinking machines, disembodied minds, silicon souls, selfish memes: we were intoxicated by metaphorical fugues, and a few pints from the local pub.
It was during these forays into artificial intelligence (AI) that I first stumbled into decision trees . A decision tree, like that shown in Figure 7-1, is a graph of choices and possible consequences. In theory, by identifying options and outcomes, and multiplying the probability and value (minus cost) of each outcome, we can reduce decisions to quantitative analysis. Of course, their utility isn't limited to humans but holds great promise for AI. After all, rational choice has long been held as a sign of intelligence. So naturally, the roots of AI, and the big wins in expert systems and game algorithms, are flush with decision trees. In fact, it was Deep Blue's ability to evaluate "leaf positions" at a rate of 200 million moves per second that enabled victory over Gary Kasparov in 1997. Before that match, the chess champion said "I'm playing for the honor of the human race." Afterward, Deep Blue remained silent.
Figure 7-1. A simple decision tree
In the last half century, the prospect of thinking machines inspired significant research and novel insight into the constitution of real intelligence. So it's both natural and ironic that Herbert Simon, a founding father of the field of AI, struck the mortal blow to the classic model of rational choice and the broad applicability of decision trees. In his 1956 landmark paper, this Nobel Laureate and A.M. Turing Award recipient employed a simple organism's search for food as a metaphor for decision-making:
He argued that within a framework of fuzzy goals, imperfect information, and limited time, our partly rational minds adapt well enough to "satisfice" but don't generally optimize. Simon's radical theory of "bounded rationality" led not only to the demise of "homo economicus," but also to appreciation for the intricacies of human intelligence, because the simple rules of chess don't apply to the complex decisions of real life.
Simon's work anticipated the difficulties of AI, though he never gave up the dream. In an interview not long before his death in 2001, he was asked whether a computer might someday deserve a Nobel. In response, Simon said: "I see no deep reason why not."