In Liars and Outliers, I use the metaphor of the Prisoner's Dilemma to exemplify the conflict between group interest and self-interest. There are a gazillion academic papers on the Prisoner's Dilemma from a good dozen different academic disciplines, but the weirdest dataset on real people playing the game is from a British game show called Golden Balls.

The more I think about the psychology of it, the more interesting it is. I’ll save my comments for the comments, because I want you to watch it before I say more. Really.

By allowing the brains of these digital organisms to evolve freely in their model the researchers were able to show that  the transition to cooperative society  leads to the strongest selection for bigger brains. Bigger brains essentially did better as cooperation increased.

"In sports competitions, teams can manipulate the result by, for instance, throwing games. We show that we can decide how to manipulate round robin and cup competitions, two of the most popular types of sporting competitions in polynomial time. In addition, we show that finding the minimal number of games that need to be thrown to manipulate the result can also be determined in polynomial time. Finally, we show that there are several different variations of standard cup competitions where manipulation remains polynomial."

"An important problem in computational social choice theory is the complexity of undesirable behavior among agents, such as control, manipulation, and bribery in election systems. These kinds of voting strategies are often tempting at the individual level but disastrous for the agents as a whole. Creating election systems where the determination of such strategies is difficult is thus an important goal. …"

"We consider the age-old problem of allocating items among different agents in a way that is efficient and fair. Two papers, by Dolev et al. and Ghodsi et al., have recently studied this problem in the context of computer systems. Both papers had similar models for agent preferences, but advocated different notions of fairness. We formalize both fairness notions in economic terms, extending them to apply to a larger family of utilities. Noting that in settings with such utilities efficiency is easily achieved in multiple ways, we study notions of fairness as criteria for choosing between different efficient allocations. Our technical results are algorithms for finding fair allocations corresponding to two fairness notions: Regarding the notion suggested by Ghodsi et al., we present a polynomial-time algorithm that computes an allocation for a general class of fairness notions, in which their notion is included. For the other, suggested by Dolev et al., we show that a competitive market equilibrium achieves the desired notion of fairness, thereby obtaining a polynomial-time algorithm that computes such a fair allocation and solving the main open problem raised by Dolev et al."

"We present two variants of Wythoff's game. The first game is a restriction of Wythoff's game in which removing tokens from the smaller pile is not allowed if the two entries are not equal. The second game is an extension of Wythoff's game obtained by adjoining a move allowing players to remove k tokens from the smaller pile and l tokens from the other pile provided l < k. We show that both games preserve the P-positions of Wythoff's game. This resolves a question raised by Duchene, Fraenkel, Nowakowski and Rigo. We give formulas for those positions which have Sprague-Grundy value 1. We also prove several results on the Sprague-Grundy functions."

HN comments are interesting:

“Something happens to companies as they get larger, where the culture of a company starts dying from a death-by-a-thousand-cuts, and then they start promoting the wrong people into upper management that seem to really poison a very good company culture. It sounds like Goldman Sachs is one of these companies.
The irony is that I think the shift in mentality came from Wall Street itself pushing the idea of "maximizing shareholder value", in the 80s. This lead to a bunch of financially positive but culturally negative (some would say sociopathic) decisions, such as closing plants that were profitable, but weren't profitable enough. I think Michael Moore had a movie on this called "The Big One".”

“Positions of power at any company, non-profit, government, or political organizations are not filled with do-gooders who want to give everyone a hug. They are filled with thick-skinned, ambitious, practical people who have learnt to say the right things at the right time. So if you're a junior exec., you will hear inspirational BS. As you join their ranks, you will hear their real thoughts. If the latter disgust you, it clearly means you aren't fit to join their ranks, not because of any lack of skills on your part but rather the difference in how you view the world.”

“I would strongly advise reading Machiavelli's Discourses on Livy on the subject of emergent culture in sovereign institutions.”

“When I worked at Microsoft in Product Support Services, our general director "got it" and encouraged us to break any rule if it helped the customer out, so long as we did so reasonably responsibly, something I took him up on to the point it seriously annoyed my direct manager. He left, and the group I worked with ended up getting shipped off to India. And given more recent discussions with Microsoft customer service, the question of "how do we deliver quality customer service" has become far less important.”

“Instead, it's an information problem. Running any enterprise the size of Google or Goldman Sachs requires trading off many competing factors. To make the tradeoff, someone has to keep all that information in their head at once. There's no other way to balance competing demands; if you keep only part of the information in your head, your decision will be biased towards the part that you've loaded into your brain. If you try to spread decision making across multiple people, the decisions will be biased towards the part that the person who screams the loudest can hold in his head (which is usually a smaller subset than optimal; it takes mental effort to scream loudly).”

“When game theory is baked into your corporate culture, this is what you get. The company starts an inevitable slide from "Do No Evil" into "Make the Best Decision You Can With the Information You Have".”

a decision theory is an algorithm for making decisions.0 The inputs are an agent's knowledge of the world, and the agent's goals and values; the output is a particular action (or plan of actions). Actually, in many cases the goals and values are implicit in the algorithm rather than given as input, but it's worth keeping them distinct in theory.

"We introduce the concept of attainable sets of payoffs in two-player repeated games with vector payoffs. A set of payoff vectors is called {em attainable} if player 1 can ensure that there is a finite horizon $T$ such that after time $T$ the distance between the set and the cumulative payoff is arbitrarily small, regardless of what strategy player 2 is using. This paper focuses on the case where the attainable set consists of one payoff vector. In this case the vector is called an attainable vector. We study properties of the set of attainable vectors, and characterize when a specific vector is attainable and when every vector is attainable."

Locomotive and farmer example.

Simon’s and Theil’s certainty equivalence property justifies a convenient algorithm for solving dynamic programming problems with quadratic objectives and linear transition laws: first, optimize under perfect foresight, then substitute optimal forecasts for unknown future values. A similar decomposition into separate optimization and forecasting steps prevails when a decision maker wants a decision rule that is robust to model misspecification. Concerns about model misspecification leave the first step of the algorithm intact and affect only the second step of forecasting the future. The decision maker attains robustness by making forecasts with a distorted model that twists probabilities relative to his approximating model. The appropriate twisting emerges from a two-player zero-sum dynamic game.

The standard theory of decision making under uncertainty advises the decision maker to form a statistical model linking outcomes to decisions and then to choose the optimal distribution of outcomes. This assumes that the decision maker trusts the model completely. But what should a decision maker do if the model cannot be trusted?

Lars Hansen and Thomas Sargent, two leading macroeconomists, push the field forward as they set about answering this question. They adapt robust control techniques and apply them to economics. By using this theory to let decision makers acknowledge misspecification in economic modeling, the authors develop applications to a variety of problems in dynamic macroeconomics.

Technical, rigorous, and self-contained, this book will be useful for macroeconomists who seek to improve the robustness of decision-making processes.

"We introduce the optimal obstacle placement with disambiguations problem wherein the goal is to place true obstacles in an environment cluttered with false obstacles so as to maximize the total traversal length of a navigating agent (NAVA). Prior to the traversal, NAVA is given location information and probabilistic estimates of each disk-shaped hindrance (hereinafter referred to as disk) being a true obstacle. The NAVA can disambiguate a disk's status only when situated on its boundary. There exists an obstacle placing agent (OPA) that locates obstacles prior to NAVA's traversal. The goal of OPA is to place true obstacles in between the clutter in such a way that NAVA's traversal length is maximized in a game-theoretic sense.…"