I have a friend who organizes board game night every few weeks (thanks LCH), and I try to attend whenever I can. Most of the time I find that game instructions often sound intractable but are in fact not all that difficult to get comfortable with if you forge ahead and just go through the motions.

Unlike computer games, there is nothing that guarantees that you are in fact playing a board game correctly. People learn to apply a set of correctness checks to the game - it can't be that the first mover always wins, it can't be that certain pieces are always ignored, and it can't be that losing a small part of the game means you would never catch up. Whenever a game seems bad, the first thing that occurs is always that you've read the manual wrongly, and the response to that is to figure out some way to play differently so that the problem does not occur.

I've always found good social games to contain an equalizing dynamic, where those who are behind are provided with certain advantages that help them to catch up, whereas those that are ahead have to be wary of defending their lead. This is related to the fact that games differ greatly in the shape of their learning curves - in games like Go, with very steep learning curves, the chances of a novice beating an expert are pretty much zero, and there is pretty much no equalizer built in.

It's obvious that homes in Singapore are smaller than those in the US and many other parts of the world. Why is this so?

Demand:

• What percent of Singaporean household income is spent on housing? How regressive is this? How do we account for purchases using retirement funds (CPF)?
• How does the demand curve for living space depend on household size and demographic group?
• Do school districts have an observable influence? What about expat areas?

Supply:

• What proportion of the cost of an apartment due to the land?
• How linear is the dependency between apartment value and apartment size?
• How much cheaper are apartments with longer commutes?
• How big is the effect of regulatory limits on living space to land ratios?
• Does it make sense for the government to manage house prices because of their role as household collateral in a fractional reserve system? Are there significant interactions with monetary policy?
• How much monopoly profit does the local building industry enjoy?

I am currently reading Approximate Dynamic Programming by Warren Powell. The book comes out of work that Powell did with Schneider National, a company that operates a large fleet of trucks in the US.

I haven't gone very far in the book, but it definitely seems very promising. The central premise that I can grasp is that it is fruitful to consider the quality of a model strictly in light of the decisions that the model is being used to make. This can be contrasted with typical surrogates like the root mean squared error or mutual information - those measures for model quality are largely derived using definitions of convergence towards total knowledge, and attempting to reach the state of total knowledge is often futile in reality.

In building practical models with practical approximations, choices in the early steps of an analysis often contain implicit knowledge about what the output from those steps is to be used for. To carry this process to its logical conclusion would be what I imagine approximate dynamic programming accomplishes - this way of dealing with ignorance trades off uncertainties in such way as to minimize the impact they have on decision quality, as measured by what you actually care about for the specific problem.