Optimality of short-term rules of thumb at long horizons for an agent-based financial market

Agent-based financial markets have been extremely successful at matching features of financial markets at relatively short horizons, but they are often silent about long range features that cross into the area of macro/financial models.  This is an area where they are often criticized by many in mainstream macroeconomics.  Their results are viewed with suspicion because individual agents might be making large mistakes that are missed by individuals concerned only with the next period's consumption, or stock return.  This paper takes a relatively simple market which is capable of matching all the usual short horizon facts, and examines comovements in consumption and asset returns at longer horizons. It is calibrated to exogenous inputs of both dividend and labor income.  Labor income is calibrated to match some of the basic distributional components from U.S. macro data.  Agents continue to follow relatively myopic rules of thumb for both portfolio and consumption decisions.   These are then analyzed from the perspective of a standard intertemporal optimizing agent to explore how close they are to this standard macro paradigm.  These experiments have some connections to other macroeconomic research which looks at just how optimal certain rules of thumb regarding behavior may actually be.  The key here is that we know that some intertemporal policy functions will look like basic myopic rules of thumb when certain assumptions are met (Campbell/Viciera(2002)).  This paper checks to see how well this works for rules which are often used in many agent-based simulations.  Would such rules look irrational when looked at through the lens of longer run intertemporal optimizing preferences? This successes and failures of this approach yield recommendations on how agent-based modeling and heterogeneous macro may be converging over time in terms of modeling tools.