Registration is now open for our two decision-maker short courses being offered for one incredibly intense week, next June.
The courses are aimed at the following quite common scenario: you or your boss is a principal and is called upon to make a consequential policy decision involving the very rapid recent changes in neuroscience or computational social science (think neurolaw, modeling the Lehman Brother’s crash, deception-detection or migration effects of climate change).
There is a proposal on the table, it requires the ability to think critically at the intersection of policy, business and the wave of new technological tools–the key is to have a good decision, informed by an appreciation for the difference between what’s real and what’s pie-in-the-sky.
We aim to educate principals and their top staff to make realistic consequential decisions informed by the latest research. Our faculty are experts in their disciplines and they all have the ability to communicate in plain English–we’ve minimized the jargon.
The venue will be Mason’s brand new Mason Inn Conference Center and your cohort of high-level fellow attendees will be as engaged as you. There will be additional ample opportunities for face-to-face social networking (still the best kind, in my opinion).
I’ve been at a meeting on the Science of Science Policy the past two days. The subject matter refers to the notion of grounding decisions on science policy more firmly in empirical data along with the notion that there might be some underlying social science theory as to how science policy decisions affect such things as national competitiveness . At the end of the day and a half, I was left with the opinion that while certainly it would be extraordinarily useful to harvest the data on federal investment in R&D across all of the agencies, it’s much harder for me to believe that we can somehow glean the rule set for making the right policy decisions from such data (although clearly heuristics might make themselves apparent).
One idea that was floated was the notion of an investigator-specific ID number that might be given at the beginning of graduate school and that would allow for better outcomes research. Imagine that you can track all of the papers, grants, patents and products that are connected to any one scientists across their entire career–independent of institution. Putting aside privacy concerns for a moment, such tracking would facilitate the assessment of where and under what conditions early investment (e.g. graduate student fellowships) resulted in massive return on investment.