1. Breakout 6: Financial Networks, Agent-Based Simulation, and Large- Scale Computation secretary: Michael Wellman moderator: William Rand

2. Modeling “The Financial System” Goals – knowledge and truth (i.e., science) – informing policy Model Characteristics Approaches Methodological Issues Data Requirements Applications / Questions

3. Model Characteristics Scope: comprehensive – endogenize as much as possible fine-grained, heterogeneous actors networked: captures linkages explicitly model information – asymmetric structure, flows emphasis on dynamics, “emergent” behavior multi-level, co-evolutionary

4. Approaches Extensions of established theoretical and empirical methodologies Simulation, Agent-based modeling Network science

5. What do we Ask of the Models? prediction counterfactual reasoning – supports mechanism design explanation / diagnosis – what just happened and why? stability assessment – also other macroscopic properties

6. Methodological Issues How do we construct believable models? – multiplicity of agents and environment fidelity  too many degrees of freedom – establishing evidentiary value of simulation results Approaches – grounded connection to well-understood theoretical models rationalize (i.e., reconcile with standard economic assumptions) game-theoretic or evolutionary stability for behavior selection – calibration with real-world data – promote replication studies

7. More Methodology connecting models at different scales – e.g., regional economies connected to mortgage valuations manage search over large space of behavioral and environment assumptions making the models “industrial strength” – model interoperability – modularity of model components – need for shared infrastructure / testbeds dealing with rare events (tails of distributions) – model risk – estimation of extreme values – econometrics of complex systems cost of large-scale computational modeling

8. Applications bond ratings – how do systemic issues bear on ratings? – how do ratings processes affect systems? – implications of open-access rating models – regulatory status of ratings and regulation arbitrage value-at-risk modeling high-frequency trading: quantifying costs-benefits dealing with thin as well as thick markets (e.g., new instruments) what is optimal rate of failure for financial firms?

9. Applications: Regulatory Tools implications of policy proposals (e.g., inter-day position limits) ability to get information to be used for emergency response how to spot qualitative changes in the structure of markets (new markets, behavior patterns) can we get a lot more usable data if we promise delays? influence of historical reporting on behavior (deterrence)