For example my high-school physics teacher, who read about me in a newspaper and then came to a panel discussion I took part in. Or Eric Weinstein, who I met many years ago at Perimeter Institute, and who has since become the unofficial leader of the last American intellectuals. Or Robin Hanson, with whom I had a run-in 10 years ago and later met at SciFoo.I spoke with Robin the other day.
Robin is an economist at George Mason University in Virginia, USA. I had an argument with him because Robin proposed – all the way back in 1990 – that “gambling” would save science. He wanted scientists to bet on the outcomes of their colleagues’ predictions and claimed this would fix the broken incentive structure of academia.I wasn’t fond of Robin’s idea back then. The major reason was that I couldn’t see scientists spend much time on a betting market. Sure, some of them would give it a go, but nowhere near enough for such a market to have much impact. Economists tend to find it hard to grasp, but most people who stay in academia are not in for the money. This isn’t to say that money is not relevant in academia – it certainly is: Money decides who stays and who goes and what research gets done. But if getting rich is your main goal, you don’t dedicate your life to counting how many strings fit into a proton. The foundations of physics may be an extreme case, but by my personal assessment most people in this area primarily chase after recognition. They want to be important more than they want to be rich. And even if my assessment of scientists’ motivations was wrong, such a betting market would have to have a lot of money go around, more money than scientists can make by upping their reputation with putting money behind their own predictions. In my book, I name a few examples of physicists who bet to express confidence in their own theory, such as Garrett Lisi who bet Frank Wilczek $1000 that supersymmetry would not be found at the LHC by 2016. Lisi won and Wilczek paid his due. But really what Garrett did there was just to publicly promote his own theory, a competitor of supersymmetry. A betting market with minor payoffs, one has to be afraid, would likewise simply be used by researchers to bet on themselves because they have more to win by securing grants or jobs, which favorable market odds might facilitate. But what if scientists could make larger gains by betting smartly than they could make by promoting their own research? “Who would bet against their career?” I asked Robin when we spoke last week. “You did,” he pointed out. He got me there. My best shot at a permanent position in academia would have been LHC predictions for physics beyond the standard model. This is what I did for my PhD. In 2003, I was all set to continue into this direction. But by 2005, three years before the LHC began operation, I became convinced that those predictions were all nonsense. I stopped working on the topic, and instead began writing about the problems with particle physics. In 2015, my agent sold the proposal for “Lost in Math”. When I wrote the book proposal, no one knew what the LHC would discover. Had the experiments found any of the predicted particles, I’d have made myself the laughing stock of particle physics. So, Robin is right. It’s not how I thought about it, but I made a bet. The LHC predictions failed. I won. Hurray. Alas, the only thing I won is the right to go around and grumble “I told you so.” What little money I earn now from selling books will not make up for decades of employment I could have gotten playing academia-games by the rules. In other words, yeah, maybe a betting market would be a good idea. Snort. My thoughts have moved on since 2007, so have Robin’s. During our conversation, it became clear our views about what’s wrong with academia and what to do about it have converged over the years. To begin with, Robin seems to have recognized that scientists themselves are indeed unlikely candidates to do the betting. Instead, he now envisions that higher education institutions and funding agencies employ dedicated personnel to gather information and place bets. Let me call those “prediction market investors” (PMIs). Think of them like hedge-fund managers on the stock market. Importantly, those PMIs would not merely collect information from scientists in academia, but also from those who leave. That’s important because information leaves with people. I suspect had you asked those who left particle physics about the LHC predictions, you’d have noticed quickly I was far from the only one who saw a problem. Alas, journalists don’t interview drop-outs. And those who still work in the field have all reason to project excitement and optimism about their research area. The PMIs would of course not be the only ones making investments. Anyone could do, it if they wanted to. But I am guessing they’d be the biggest players. This arrangement makes a lot of sense to me. First and foremost, it’s structurally consistent. The people who evaluate information about the system do not themselves publish research papers. This circumvents the problem that I have long been going on about, that scientists don’t take into account the biases that skew their information-assessment. In Robin’s new setting, it doesn’t really matter if scientists’ see their mistakes; it only matters that someone sees them. Second, it makes financial sense. Higher education institutions and funding agencies have reason to pay attention to the prediction market, because it provides new means to bring in money and new information about how to best invest money. In contrast to scientists, they might therefore be willing to engage in it. Third, it is minimally intrusive yet maximally effective. It keeps the current arrangement of academia intact, but at the same it has a large potential for impact. Resistance to this idea would likely be small. So, I quite like Robin’s proposal. Though, I wish to complain, it’s too vague to be practical and needs more work. It’s very, erm, academic. But in 2007, I had another reason to disagree with Robin, which was that I thought his attempt to “save science” was unnecessary. This was two years after Ioannidis’ paper “Why most published research findings are false” attracted a lot of attention. It was one year after Lee Smolin and Peter Woit published books that were both highly critical of string theory, which has long been one of the major research-bubbles in my discipline. At the time, I was optimistic – or maybe just naïve – and thought that change was on the way. But years passed and nothing changed. If anything, problems got worse as scientists began to more aggressively market their research and lobby for themselves. The quest for truth, it seems, is now secondary. More important is you can sell an idea, both to your colleagues and to the public. And if it doesn’t pan out? Deny, deflect, dissociate. That’s why you constantly see bombastic headlines about breakthrough insights you never hear of again. That’s why, after years of talking about the wonderful things the LHC might see, no one wants to admit something went wrong. And that’s why, if you read the comments on this blog, they wish I’d keep my mouth shut. Because it’s cozy in their research bubble and they don’t want it to burst. That’s also why Robin’s proposal looks good to me. It looks better the more I think about it. Three days have passed, and now I think it’s brilliant. Funding agencies would make much better financial investments if they’d draw on information from such a prediction market. Unfortunately, without startup support it’s not going to happen. And who will pay for it? This brings me back to my book. Seeing the utter lack of self-reflection in my community, I concluded scientists cannot solve the problem themselves. The only way to solve it is massive public pressure. The only way to solve the problem is that you speak up. Say it often and say it loudly, that you’re fed up watching research funds go to waste on citation games. Ask for proposals like Robin’s to be implemented.
Because if we don’t get our act together, ten years from now someone else will write another book. And you will have to listen to the same sorry story all over again.