  Arthur's paradigm of the El Farol bar for modeling bounded rationality and
inductive behavior is undertaken. The memory horizon available to the agents
and the selection criteria they utilize for the prediction algorithm are the
two essential variables identified to represent the heterogeneity of agent
strategies. The latter is enriched by including various rewarding schemes
during decision making. Though the external input of comfort level is not
explicitly coded in the algorithm pool, it contributes to each agent's decision
process. Playing with the essential variables, one can maneuver the overall
outcome between the comfort level and the endogenously identified limiting
state. Furthermore, we model the behavior of the agents through the use of an
expression that scores the local attendance states available to the agents. It
incorporates a single parameter that weighs the relative contributions that
originate from the external and internal limiting factors. Solving this
expression analytically as well as numerically using the Metropolis Monte-Carlo
technique enables us to attribute statistical-thermodynamical meanings to the
essential variables identified with the agent-based model, and to gain physical
insight to the bounds observed in the behavior of the agents. The power of the
analytical approach is validated by obtaining a one-to-one correspondence
between the agent-based model and the analytical approach within a wide range
of thresholds using a single parameter for a given selection criterion. The
origin of discrepancies between the two models appearing at extreme thresholds
is tracked to the shifts in the distributions of algorithm types actively
utilized by the adaptive agents.
