Heuristics and policy-making

Main article: System modeling

Heuristics are an inevitable part of the decision-making aspects of the community ratings system. Information overload and policy detail will be too much for most people to handle in a direct democracy, even the well-informed. We've discussed heuristics in the context of weight assignment as one solution. This, in turn, leads to the rise of expert organizations that can help. But going down this path too far leads us closer to the top-heavy and ineffective systems we have today. We would generally rather provide as many technological tools to individuals to make it possible for them to contribute meaningfully. The ratings system, as an information system, will help with this by providing aggregate opinions, polling information, and so on. But these only go so far. We pose here that the cognitive burden of direct democracy can be broken with sophisticated simulation that meet decision makers where they are.

Consider how our political process works today. Most people are ignorant of the policy choices facing them as citizens. They are not sure which politician’s agenda actually aligns with their interests. A politician, sensing this, will offer up simple fixes, strong stances on meaningless but emotional issues, or just a nicely packaged campaign of soundbites and entertainment. The voter is supposed to assess their actual desires and needs against the message of a political campaign run by marketing experts. Most American’s, as a result, vote randomly, as we’ve discussed. We can excuse this by saying it is everyone’s choice to be informed (or not) but this seems unsatisfactory for a society aiming at inclusive democracy.

But what if there were a better way? We suggest a direct democracy where the voter would have, presumably, a one-on-one relationship with policy. So no more self-interested politicians marketing to the public. This would help but is also likely to break the cognitive capabilities of even well-informed voters. One answer to this is using simulation as a heuristic. If a voter says they want good jobs then simulation can measure that desire against simulated policies. The policy vote itself doesn’t actually have to be made by the voter. They can, in effect, delegate their vote to the outcome of a simulation which has placed the availability of good jobs as the highest priority among a variety of policy options.

NPR recently interviewed David Madland from the American Worker Project at the Center for American Progress. He in turn described talking to workers at a new EV battery plant in TN who mentioned that the stable, well paying job they just got at the plant was the best one they’ve ever had. They mentioned being able to take their families on vacation. They were asked who they have to thank for these new, good jobs. They mentioned Ford (the company who owns the plant) and their union. These certainly deserve some credit but, from a policy perspective, the reason is The Infrastructure Investment and Jobs Act and the Inflation Reduction Act, both passed by a bipartisan effort in Congress in 2021-2022. But the workers had no idea and its not really their fault. They get little to no information on this crucial policy link. There’s no sign in front of the plant that mentions these two acts. There is no marketing campaign linking every infrastructure effort (there are thousands) back to these acts or the politicians who were responsible for them. Perhaps there should be, but there isn’t, and even if there were it is likely that the workers in TN would still not know about it.

It is fairly easy to surmise that ordinary people will never have the policy knowledge needed for informed voting. Politicians from all sides will probably tell them their policies will result in what they want but how is the ordinary person to know the difference? The best they can do is state their desires and turn their vote over to an objective analysis of policy. In this case, it takes the form of careful modeling and simulation.

Note that there is no partisanship in any of this. Associating a simulated policy to a party or ideology is a separate cognitive step, perhaps interesting to the ideologically minded, but one that can be safely skipped for most.

By relating policy choices directly to people’s needs through some heuristic mechanism we can incorporate the wishes of the generally uninterested voter. We might find that doing this leads to some curiosity about policy in general. Still, as a matter of course, any true democracy should try to meet people where they are.