GECCO is one of the largest conferences in the field of evolutionary algorithms, and I’m told there are something over 400 people attending this year in sultry Philadelphia. The first two days are often the calmest, broken up into a “mere” seven or eight simultaneous tracks, about equally workshops where a half-dozen topic-specific papers are presented and discussed, and tutorials in which introductory and advanced techniques are reviewed. Before the early starts, during the two-hour lunch break (if there is one), and after hours we participants self-assort and chat and catch up, like any technical conference I suppose.
It’s an interesting thing to watch from the inside. The social networks, the cliques and components forming and re-forming. I can’t help but feel I’m watching turf wars on the ground: all of us old influencers with our various Schools of Martial Arts vying for attention, revisiting old scores and reminiscing about which thing happened when, joking about In the Old Days at the same time we eye the young bloods keenly and try to steer them towards or away from Right Thought and Practice.
This is a dilute field, if it’s a field at all. One or two people in each institution, with a couple-three graduate students each. In the old days, there was still a defensive air about it, like we used to hear at Complex Systems research conferences. We’ve moved (a bit) past that stage of justifying the approach as such, convinced ourselves and our peers there’s something useful to be done with “biased random guessing”, so now the tribes are splitting along not-unexpected lines as they think about what they should seem like. Should this thing I’m doing sound like Operations Research, clad in formal provable correctness and rigorous theoretical justification? Should it sound like Artificial Intelligence, with agents believing and intending and desiring to move blocks and identify spam? Should it be a form of generative art, a way of inspiring and provoking me to new works? Should we consolidate our resources, like the Machine Learning people did, and start benchmarking so we can “better compare” results, or build tools that make it “easier” for “non-technical people” to “solve problems”?
And of course the lumping and splitting goes on, as ever: But this is just another example of that, a special case. This is new (it must be, if you’re presenting it); this is old (it must be, if you are to be given credit); that is this (it should be, if we’re to understand one another); this is not that — nothing is ever anything else. Where “this” and “that” are works, techniques, experiences, guidelines, rules, habits, terms.
Nobody talks about the community in which they work. Not this community of conference-goers, but the community of people who might someday actually want their cancer cured, or their stocks traded. The academic habits of the Cold War — deep resource deprivation, the Artificial Intelligence paradigms, the rampant cultivation of Lone Genius as the one route to greatness — they drive so many into their own work and away from helpful collaborations. There are collaborations within the families (the MIT family, the Virginia family, the Illinois family, the Stanford family, the Michigan family with its wide-ranging limbs), but still I never see people talking about how a problem becomes a project, nor how a “result” becomes a useful furthering of some conversation.
There’s discussion of subjective experience, of course. Some nice ones. But even these lie within the boundaries of the Technical Works: I was surprised to see it doing this; I tried X but eventually Y was better.
What I’ve found so far is that my old colleagues work as though providing self-contained software were of use to anybody. They talk as though publishing self-contained repositories of data and algorithms were of use to anybody. They think, I’m afraid, that the places human beings interact with their work should be limited to the production of “requirements” and the acceptance of “results”.
They do not speak of human context.
I hope they get over it. I would like them to find something outside themselves and their particular School of Martial Arts and their own turf that brings them together in a “track”. How do we cope with fear? With hubris? How does one work, when one is working in service of some other person’s aim?
I have standard questions I ask in almost every talk I attend: Why do you ignore the other objectives? Why do you use that habit, that library? Why do you measure that thing, and not the real thing you’re doing?
I don’t ask these questions to find out their technical justification. I’m not quizzing them on how well they’ve learned to write a multiobjective non-domination algorithm, or read and write from a database. I’m asking them because I’m still waiting for one to say, “Because when we discussed it with our colleagues, the people whose question this is, they wanted to try this. It was best at the time. It was something so we could deliver results and learn together.”
I want somebody to say, “The customer saw value in it.”
But so far I fear the “customer” is represented in this work as a mere tradesman, or worse a “layman”. Somebody you help only after taking their problem away from them, Being Very Smart, and giving the Right Answer back to them so they can use it.
Such a Waterfall world. So confident, even in the face of hundreds of hours of talks discussing why things are hard and complicated and slow.
These are excuses for failing to deliver something to a person who can use it. Of course they’re cultural excuses, Cold War excuses, Academic excuses based on “significantly advancing the field”. But they justify a view in which work is yours, and problems belong to other people.