In our ever expanding algorithm-focused universe calculations of risk are becoming common place. Financial institutions often come to mind as determinations to approve mortgages, car loans, or even credit cards are algorithmic and risk based assessments. Investment risk measures your ability to tolerate potential losses in the face of higher anticipated returns at the expense of higher volatility or risk. Can we apply this to healthcare decisions as well? What would a diversified portfolio in healthcare even look like?

These are laws enacted that are self-sustaining. For example, you don't drive through red lights into oncoming traffic because you fear getting a traffic ticket--the real reason is you don't want to die.

Decisions that are good for individuals can sometimes be terrible for groups--In a Nash equilibrium, every person in a group makes the best decision for herself, based on what she thinks the others will do. And no-one can do better by changing strategy: every member of the group is doing as well as they possibly can. In the case of the prisoners' dilemma, keeping quiet is never a good idea, whatever the other mobster chooses.

Since one suspect might have spilled the beans, snitching avoids a lifetime in jail for the other. And if the other does keep quiet, then confessing sets him free. Applied to the real world, economists use the Nash equilibrium to predict how companies will respond to their competitors’ prices. Two large companies setting pricing strategies to compete against each other will probably squeeze customers harder than they could if they each faced thousands of competitors. --What is the Nash equilibrium and why does it matter? The Economist.

The solution may be advances in blockchain and factual time stamped compliance and performance. Increased transparency has the potential to create an immutable ledger of cooperation and improved outcomes. Read more at Blockchain and Nash equilibrium in Healthcare.

--Computational complexity theory sheds new light on the“bounded rationality” of decision-makers. Approximation guarantees, originally developed to analyse fast heuristic algorithms, can be usefully applied to Nash equilibria. Game Theory Through The Computational Lens

## Time Trade-Off Valuation of Health Outcomes--link to article

The solution is to select domains of interest instead of attempting to solve large-scale problems.

I find academic lectures to be helpful in understanding algorithms and game theory. If you want a deeper dive into autonomous and strategic interests in a system described by algorithms Tim is a brilliant lecturer.