Computational Economics

1. Computational Economics Elan Pavlov

2. Central problem statement • How to design mechanisms and algorithms which cause selfish players to act as if they are interested in the global good. • The problem is people will often be better off if they act selfishly. We need to align their choices to global good. • Solve problems with consideration for their real complexities.

3. Computational economics • Computational economics is a form of economics which is constrained by the demands of computer science. • Classic economics often just proves that something can be done. Computational economics tries to actually do it.

4. New field • The field started around 1999. Currently there are very few dedicated conferences for computational economics. • The field is growing very fast.

5. Why is computational econ important? • The Internet has spawned new problems such as: • Allocation of advertisements. • Auctions with budgetary constraints. • Multi-mechanisms run by different companies. • Strong emphasis on problems where the input is not known from the start.

6. New problems • Auctions which could have been handled easily before are now harder to do. • The standard economics approach don’t scale well. • A need for quick decisions.

7. The field takes from Economics • Problems. • Definitions: players, equilibrium, strategy space • Some worldview: Welfare, revenue. • Some tools: • Game theory. • Auction theory. • Mechanism design. • Price of anarchy.

8. But also from computer science • Mainly tools: • Online algorithms. • Approximation ratios. • Graph theory. • Reductions. • Optimizations. • But also demands: • Polynomial time. • Hardness.

9. What we take from real life • Relevant problems. • Feedback. • Motivation.

10. Various problems • Keyword auctions. • Dragon kill points. • Cellphones for selfish users. • Parking auctions.

11. Keyword auctions

13. We can do better • Example: Two advertisers with values per click 1 and budget 1. Two keywords with probabilities:

14. Currently

15. Better allocation

16. Keyword 3 Keyword 2 Keyword 4 Keyword m Keyword 1 Advertiser 1 Advertiser 2 Advertiser 3 Advertiser n A generalized min cost flow Target (am,0,1) (a1,0,1) (∞,-v1,1/p11) (∞,-v2,1/p2m) (n2,0,1) (n3,0,1) (n1,0,1) (nn,0,1) Source (capacity, cost, scaling) (Not all edges are drawn)

17. Benefits of transferring demand • Better welfare (possibly twice as good) – happier advertisers. Exact values depend on advertisers. • Better revenue (possibly much better). • Happier users (on average). A triple win for the search engine, advertisers and users. Potential drawback: Loss of control for advertisers.

18. Other problems with ad auctions • Estimating Click through rate. • Buying ads. • Selling by impression, click-through or conversion. • What happens when there is competition for advertisers? How do you maximize revenue?

19. New economies • There are a lot of new social networks with their own economies. Second Life has created a (real-life) millionaire. WoW has an economy that is estimated to be larger than many third-world nations. You can buy hundred of items from Guild Wars on eBay.

20. Why are new economies interesting? • These economies are different from classic economies in that: • They can be easily measured. No black economy or gray economy. • They can be easily manipulated (for example supply of land in Second Life). • They can be controlled and designed better. For example, money supply, ways in which people can create wealth etc.

21. Dragon kill points • In MMORPG there are encounters which yield items. The harder the encounter the better the item is. • Most hard encounters demand the participation of a large number of players to successfully complete the encounter (and get the treasure). • Since the treasure is unsplitable the question arises of how to allocate the item.

22. An auction but how? • The basic idea is to auction off the item to the participating players. • The main challenge is a temporal one: Given a fixed set of players (the guild) the demand is fixed and since the supply can go up – the value of items decreases.

23. Temporal problems • This means that players have to make a decision when to bid. • The problem is also online. • We have several results for different cases (depending on what distributional assumptions we make).

24. Algorithm • Conceptually the algorithm works by renting out items. We look at the supply/demand per day and charge for that day based on that supply/demand. • The price fluctuates (decreases) for each day. • In practice items are not transferred between players.

25. Power for cell phones • Power usage in cell phones is a function of the distance to the destination. • Depending on the terrain the power usage is between distance2 to distance4. • Why not use multi hops?

26. Possibility 1 Example Peer Caller Destination Peer

27. Example Peer Caller Destination Peer

28. Peer Caller Destination Peer

29. The problem • People are selfish and the person who agrees to forward can have her cellphone battery depleted faster.

30. Our solution • We offer a simple guarantee: “If you agree to forward for others your battery will always last at least as long as if you don’t”. Usually, it will last three times as long. • Based on a simple system of debt. People will forward only if they owe the system. • We also have debt forgiveness. • (However we have a catch-22).

31. Simulations - adoption

32. Simulations – power usage

33. Lack of parking is a global problem. 1st order effects include lost time and lots of frustration. 2nd order effects are worse: congestion, pollution, public health.

34. Detroit Metro Airport Michigan Theater, Detroit

35. Existing Services

36. Auctions • Auction are an efficient mechanism for determining value, but not all auctions are created equal! • Current auction mechanisms allocate parking to the person with the highest value (willingness to pay).

37. Problem with current auctions • Current mechanisms take no account of the duration of the requested parking. • This can prevent several people from parking thereby leading to higher congestion. • Worse, this model incentivizes squatting and extended parking.

38. The core idea • The core idea is that for every parking lot there is a capacity and a currently used fraction of capacity. • We also have estimates on the lower (P0) and upper bounds of valuations (or more accurately r=log Pmax). • The price for any duration between [t1,t2] is ∫P0rcapacity-usage if a drive is willing to pay the price then they can park.

39. Prices • As demand approximates supply the price approximates the upper bound on value. • Although the problem is both a packing problem and an online problem the total welfare approximates the maximal possible welfare. • We might not be able to solve the problem but we can get within a factor 2 of the optimal.

40. Future areas for the field • Broad questions: • How does computational economics interact with machine learning? • What happens to online algorithms with selfish users? • New kinds of prediction markets? • Markets and economies of online games. • Markets with intermediaries (such as eBay) • Auctions with probabilities.

41. In summary • The field of computational economics is a new field which has many interesting directions in which to develop. • The goal of the field is to understand/invent ways in which people can share resources for global good. • The question is how to design, analyze and build mechanisms to enable sharing.

42. Q & (hopefully) A Sample question: How do you find true love? Answer: