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Dynamics of Rule Revision and Strategy Revision in Legislative Games

Dynamics of Rule Revision and Strategy Revision in Legislative Games. Moshe Looks Ronald P. Loui Barry Cynamon Washington University in St. Louis, USA. Basic Idea. Legislators don't always Say what they mean nor Mean what they say Hart: there is a limit to the use of language…

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Dynamics of Rule Revision and Strategy Revision in Legislative Games

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  1. Dynamics of Rule Revision and Strategy Revision in Legislative Games Moshe Looks Ronald P. Loui Barry Cynamon Washington University in St. Louis, USA Looks/Loui/Cynamon

  2. Basic Idea • Legislators don't always • Say what they mean nor • Mean what they say • Hart: there is a limit to the use of language… • Sometimes the rule-makers don't even agree • Be deliberately vague • Toss the issue to the courts Looks/Loui/Cynamon

  3. Legislation is Worth Studying • Rules change • Often they change in response to: • Agents behaving badly • Agents discovering unintended strategies • North there is institutional learning… • Legislate to perform strategy extinction Looks/Loui/Cynamon

  4. How to Study Legislative & Institutional Dynamics? • Multi-agent systems simulation • Can we build a model that exhibits the interesting phenomena? • Agent modeling • Institutional modeling • Plausible dynamical modeling • Would anyone (outside AI) be able to work with rules: • As text? • As logic fragments? • As procedures? Looks/Loui/Cynamon

  5. Idea! • All edict takes the form of • an objective function on k variables • to be maximized • Legislative revision = change of function • Legislative abridgement = projection • Onto subspace • I.E., use only a subset of the variables Looks/Loui/Cynamon

  6. Target Phenomenon I • Tenure-granting colleges often publish rules: • Will count journal publications • Will count student evaluations of teaching • Will count amounts of external research funding • Legitimately interested in • Productivity • Intellectual impact • Teaching ability • Published criteria: • Observable • Apparently precise Looks/Loui/Cynamon

  7. Target Phenomenon I • Those seeking tenure subvert the spirit of the rules by: • Joining long co-author lists • Reporting research in minimal-publishable-units • Avoiding teaching difficult courses • Giving inflated grades • Doing research for the sake of funding • Adding their names as Co-PI to big projects • In the worst case, there is misdirection • Papers written for the resume, not for the scholarship • Teaching aimed at good feedback, not long-term student growth • Research aimed at getting funding, not intellectual impact Looks/Loui/Cynamon

  8. Target Phenomenon I • Tenure Committees Respond by • Normalizing papers by author count • Evaluating the five best publications • Measuring student performance objectively • Capping funding amounts that can be reported • Requiring co-PI's to show students supported on funds • Candidates for tenure respond in situ to new reqirements • They don't toss their resumes & start from scratch Looks/Loui/Cynamon

  9. Target Phenomenon II • Tax regulations seek to encourage charitable deductions including (as cases are decided): • Donations of books to book sales • Donations of cars to non-profit organizations • Donations to arts performance organizations Looks/Loui/Cynamon

  10. Target Phenomenon II • Taxpayers respond by • Buying books for the purpose of donating them • Donating cars that do not run • Donating to performance companies in exchange for free tickets • Over time, through legislative misdirection • agents optimize the wrong function Looks/Loui/Cynamon

  11. Target Phenomenon II • Tax regulators respond by • Requiring receipts showing purchase amounts • Allowing deduction for only car’s value realized on sale • Reducing amounts of donations by any quid-pro-quo considerations • Taxpayers respond again by donating less • New abridgement repairs short-term misdirection • Successful strategy extinction or scenario extinction • In time, different legislative misdirection Looks/Loui/Cynamon

  12. Our Model • There is a veridical value function: • V(x, y, z, …) • “known” to the legislators • At any time, there is an abridgement of V: • A(x, z) • A function of fewer variables • More generally, use a projection of V • public Looks/Loui/Cynamon

  13. Our Model II • At any time, an agent’s strategy/position is • A point in V-space • (9, 10, 1, …) • With de jure value A(9, 1) • With de facto value V(9, 10, 1, …) • Agents occupy admissible positions • E.g., declare that (0, 0, 0, …) is prohibited • Admissibility is not known to all • Admissibility is discovered through search • Admissibility can also change with time Looks/Loui/Cynamon

  14. Our Model III • A legislature can respond: • Change the set of admissible positions • Change the function A (OUR FOCUS) • An agent can respond: • Search for point with higher A-value • Learn from other agents • Where are the high A-values • Where are the admissible points Looks/Loui/Cynamon

  15. Simulations • Example: Greedy Non-Omniscient Agents • Look at neighborhood around current point • Move to highest point with highest A-value • Bound how much they can move • Example: Imitative Non-Omniscient Agents • Move toward average of others • If it is better than where you are • Bound how much they can move Looks/Loui/Cynamon

  16. Looks/Loui/Cynamon

  17. Simulations • Example: Incentivizing the Average • Revise A so AverageAgent maximizes V • Project V onto line between global opt and avg position • Mix(A,A') to bound difference • Example: Extinguishing the Worst • Find d* = maximum V  A • Choose A' to minimize A'(d*) • Diff(A,A') is bounded Looks/Loui/Cynamon

  18. Simulations • How quickly can the legislator act? • Dominant legislature • Revises as quickly as agents, bounds generous • Parity • Revises as quickly as agents, bounds on par • Dominant agents • Agents revise more quickly, bounds on par Looks/Loui/Cynamon

  19. Looks/Loui/Cynamon

  20. Background Functions • In addition to V, A, add • B which models • The public "spirit" of the laws • E.g., B is a time-average of A over a period • Agents who maximize B are more robust to changes of A • B contains additional knowledge about A Looks/Loui/Cynamon

  21. Background Functions • Taxonomy of agents: • A-maximizer at the expense of B is a rat • A-maximizer s.t. high B is a literalist • B-maximizer s.t. high A is a wolf • B-maximizer at the expense of A is an idealist • Imitative A-maximizer is a sheep • Novel A- but not B-maximizer is an exploiter • Novel A- & B-maximizer is a producer Looks/Loui/Cynamon

  22. Conclusions • Result? A model with rich and appropriate dynamics • Main contribution: depicting legislative phenomena in mathematical economics (or ICMAS) clothing • Would like A-B-V triad to be memorable • Main idea: there must be a reason to revise • Agents learn • Legislative misdirection accrues • Much legislation is repair of old abridgement Looks/Loui/Cynamon

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