Tag Archives: Combo

Em City By Combo Auction

Yesterday I outlined how combinatorial auctions could help our cities better coordinate their land use and utility capacity, without granting great discretion to a central power. But I ended with:

It would be very hard to get agreement to change to this system from today’s system of property rights and regulatory restrictions. I despair of it happening in our comfortable and change-averse cities. So we might have to wait until a big disruption creates lots of other change. (more)

Two years ago I pointed to a big-enough future disruption:

Rich stable nations … feel little inclination to consider big disruptive changes. … This frustrates rich-nation would-be-rebels like me who see our business, legal, political, etc. institutions as far from optimal. … If you long to say “come the revolution,” you might wait three to fifteen decades for the “em rev“, the whole brain emulation revolution. …

Rapid [em] growth will require huge rapid changes in economic organization, and supporting changes to business, legal, and political institutions. … Locations vying to be one of those [first em] centers may be open to big institutional change. … So if you have a favorite radical change you’d like the world to consider, you might give some thought to how your change could support a local em rev. (more)

The first em cities may be especially open to change regarding how cities are run. How might combinatorial auctions help them?

Here are my best guesses about (mid-em-era) em cities. City centers would mainly house computers, mostly running brains, and supporting infrastructure, e.g., power, cooling, structural support, part swapping paths, security, leakage containment, etc.

City centers would mostly house ems in virtual bodies doing office work, meeting often with other city workers. In most meetings, brains would stay put and just send signals; physical movement would be much rarer. Em minds would be sped-up relative to human minds as far as possible, until doubling an em’s mental speed much more than doubled its computing costs.

Outside of city centers there would be more ems in physical bodies, mostly small, helping with physical activities such as mining, harvesting, manufacturing, transportation, dumping, etc. Air cooling in the periphery would give way to water cooling closer in, and perhaps molten salt cooling very close.

All this would put a huge premium on inner city computer speed, density, and bandwidth. Cities would be very 3D, and city center computers would likely have very small physical structures generating lots of heat, making cooling crucial. Also important would be power sources, and physical paths for the replacement of devices and parts.

Today big computing centers are centrally planned, mostly with uniform parts and regular structures. But this level or coordination is may be infeasible for large cities, where diverse organizations make coordination expensive and change hodge-podge. In such a context, combinatorial auction might help improve coordination.

In am em city combinatorial auction, bids for locations could specify:

  1. spatial volume, shape, and orientation
  2. part swapping portal locations and sizes
  3. line of sight to outside, or to specific parties
  4. surface temperature and chemical corrosively limits
  5. amount and form of power and cooling, with price limits
  6. specific chemicals piped in, fluid garbage piped out
  7. communication distance from other particular residents
  8. time delay and expense to move hardware out and in
  9. support force tensors (including weight) get, support can give
  10. max stress-strain to support during earthquake
  11. limits on incoming, outgoing vibration distributions
  12. chances of incoming, limits on outgoing, leakage
  13. chance of explosive destruction, correlation with distant backups
  14. legal rules covering disputes with neighbors
  15. time commitments on each of these, and penalties for violations

As with cities today, winning allocations would say who gets what spaces with what supporting utilities, limits, etc. Competitive utility suppliers could also bid their prices to use particular spaces to supply particular utility amounts to particular locations. and futures markets about future winning bids might help estimate opportunity costs of commitment. Auction revenue could pay for utility fixed costs and repay city investors, and futarchy might choose the basic auction rules.

Yes, there’s a lot we don’t know about the future, and I could get some things wrong here. Even so, it seems worth thinking about what the future might be like, and when big institutional changes might be feasible.

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City By Combo Auction

I recently heard talks by Ryan Avent, author of “The Gated City”; and Matt Yglesias, author of “The Rent Is Too Damn High.” Both agreed that the inefficiency of urban land use has increased greatly in the last few decades, at least in the biggest US cities like New York. Most of the infrastructure that makes such cities great was made during the era of political machines, when a dominant party had great power to coordinate activities city wide.

We dismantled political machines out of revulsion for unchecked power. But now our cities fail to coordinate. Local zoning boards may often coordination to benefit local interests, but they also often fail to coordinate with distant boards to achieve city-wide gains. For example, when each area limits density to keep out poor folks, such as via lot size and height rules, the city fails to provide a place for needed poor workers. How can cities better coordinate, without the vast corruptible discretion of political machines?

Imagine for simplicity that we are starting a new city, or at least a new city area, from scratch. Some developers are thinking about building various types of housing, targeted at various types of residents. National chains are considering locating stores and food outlets. Employers and private schools are thinking of locating there as well. The question is: who will build where, and what utilities such as roads, power, water, sewer, internet will be built where to supply this new area?

One solution is to have a single developer initially own the entire area, and negotiate directly with all these parties. Political machines once filled a similar role. Today politicians and boards for zoning and utilities try to fill such roles. But there are other options. Yesterday I explained the concept of a combinatorial auction, using the example of assigning offices when moving to a new office building. Today I’d like to elaborate on how such a mechanism could be used to coordinate urban land use and utilities.

It would look a lot like the scenario I outlined yesterday for allocating offices. Just on a bigger scale. Each party would submit bids describing their willingness to pay for various combinations of land features. Bids could specify:

  1. Land area, soil type, topography.
  2. Views (or not) of mountains, ocean, factories, etc.
  3. Distance (meters or road time) from residents, shopping, jobs, schools, parks.
  4. Local limits on ambient sound, smells, light, etc.
  5. Limits on nearby resident demographics, and the “class” of shops.
  6. Limits you are willing to supply for own sound, smell, demographics, class.
  7. Utility services (e.g., power, water, internet, trash) required, at what prices.

A computer could then search for a max value set of mutually compatible bids. The winning assignment might specify:

  1. Which parties get what land for which uses.
  2. Local limits on building heights and appearances.
  3. What land is used for utilities such as roads, power generation, etc.
  4. What utility capacity is supplied at each place, and related price limits.
  5. Local policies limiting local behavior making sound, smell, light, etc.

Revenue from winning bids could help pay for city services. The cost of utility services could be included either via a cost model, or via bids from competing service suppliers. Adjustments might be made for an expected underbidding for shared resources. Bidding assistants and iterative bidding might keep bid elaboration efforts manageable. Calculation of max value bid sets might even be farmed out to competing calculators (who keep bids secret).

Now of course real cities are not usually built all at once, so we’d need to adapt the above process to incremental city change. Bids for each new time period could request options for similar future use at the same price, and specify a compensation due if that option is revoked. Futures markets estimating future winning bids might help determine the opportunity cost of awarding such commitments.

Yes there might still be opportunities for corruption and favoritism in this system, such as by leaking secret bids, and biasing the auction rules. But this still seems far less corruptible than today’s system. And we might use futarchy to take away even more opportunities for corruption.

Yes, it would be very hard to get agreement to change to this system from today’s system of property rights and regulatory restrictions. I despair of it happening in our comfortable and change-averse cities. So we might have to wait until a big disruption creates lots of other change. More about that tomorrow.

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Office By Combo Auction

Twenty years ago when I worked at NASA Ames, our group moved to a new office building. We put up a map of the new offices, and invited folks to put their name on a office they liked. People were ranked by seniority, and a higher ranked person could bump a lower ranked one from an office. People changed their office assignments based on what office they liked and who they wanted to be near until changes slowed to a trickle. And that was our new office arrangement.

Now imagine a much more elaborate system. Imagine that all the workers in your office are moving to a new building, requiring a new assignment of floor space to offices, conference rooms, copier/printer rooms, lunch rooms, hallways, etc. Now imagine that this assignment is done by combinatorial auction.

That is, imagine each person in your office has a budget of cash or bidding points, and submits bids saying how much he or she would pay for various office scenarios. Such bids can express values for:

  1. Whether one sits in an open cubicle or closed room, and how many officemates.
  2. Office features like size, windows, carpet, lights, climate controls, elevation, etc.
  3. Distance of office from entrances, bathrooms, conference rooms, lunch rooms, etc.
  4. Distance of office (in time or space) from the offices of other particular associates.
  5. Utilities like wired internet, big power plugs, or paper mail delivery.
  6. Local policies like if allow loud conversations, or food eaten at desk.

Given such bids, a computer could search for the office assignment that achieves the highest total bid value. Such an assignment might say:

  1. Who sits in which office.
  2. Which rooms are assigned as offices, conference rooms, lunch, printers, etc.
  3. If changes can be made, each office’s carpet, lights, windows, climate controls, etc.
  4. If changes can be made, what internet, power, etc. are supplied to each office.
  5. If partitions can be moved, the number and size of rooms.
  6. For each area, policies on loud conversations, food at desk, etc.

When choices like differing carpets vary in cost, cost functions could let one seek the assignment that maximized the total bid value minus costs. Such cost functions could express scale economies, such as it being cheaper to give all rooms the same carpets. When management cares about office arrangements beyond satisfying office workers, management preferences could be expressed in management bids, or in constraints on the final arrangement.

To save workers from having to express too many bid details, the process can be iterative, always showing a tentative assignment given the last round of bids, so bid elaboration efforts can focus on aspects that make a difference. (Bids themselves would stay secret.) Bidding assistant software might also infer preferences from user ratings of past or hypothetical offices.

Now even with a perfect choice of who gets what bidding budget, this process isn’t at all guaranteed to give an optimal office arrangement. For example, workers would likely underbid for shared resources like conference rooms; they’d want them but rather that others pay for them. There is now a whole academic field of “mechanism design” that studies the general problem of choosing rules for how such “direct revelation” bids are expressed, how they are updated across rounds, who wins what in the end, and who pays how much.

And yet, even the simple process described above would get a lot of things right, things that most offices get pretty wrong. After all, workers would actually get offices that had a consistent relation to their office preferences. Which makes it a shame that we don’t do this sort of thing more.

Yes, I realize that such computer-based solutions have not been feasible until recently, that there is work to be done to make them easy, and that innovation takes time. I also grant that bosses may see this as threatening their power, and that we may have social norms against using “money” in such a “personal” context (even in business!).

I also don’t want to give the impression that combinatorial auctions are my idea. I worked on them during ’93-’95 as a grad student under John Ledyard and David Porter. There is now a whole academic field of combinatorial auctions. See this book, its intro, and also articles on applications to environmental offsets, spectrum, airport landing slotsland consolidation, purchasing, and procurement of trucking and school lunches. See also a sf story.

And yes, assigning offices is far from the biggest problem we face in this world. This post mainly uses it as a vivid example to introduce the concept of combinatorial auctions. I’ll elaborate on a bigger application tomorrow.

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