Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Models of Level-0 Behavior for Predicting Human Behavior in Games   Behavioral game theory seeks to describe the way actual people (as compared
to idealized, "rational" agents) act in strategic situations. Our own recent
work has identified iterative models (such as quantal cognitive hierarchy) as
the state of the art for predicting human play in unrepeated, simultaneous-move
games (Wright & Leyton-Brown 2012, 2016). Iterative models predict that agents
reason iteratively about their opponents, building up from a specification of
nonstrategic behavior called level-0. The modeler is in principle free to
choose any description of level-0 behavior that makes sense for the setting.
However, almost all existing work specifies this behavior as a uniform
distribution over actions. In most games it is not plausible that even
nonstrategic agents would choose an action uniformly at random, nor that other
agents would expect them to do so. A more accurate model for level-0 behavior
has the potential to dramatically improve predictions of human behavior, since
a substantial fraction of agents may play level-0 strategies directly, and
furthermore since iterative models ground all higher-level strategies in
responses to the level-0 strategy. Our work considers models of the way in
which level-0 agents construct a probability distribution over actions, given
an arbitrary game. Using a Bayesian optimization package called SMAC (Hutter,
Hoos, & Leyton-Brown, 2010, 2011, 2012), we systematically evaluated a large
space of such models, each of which makes its prediction based only on general
features that can be computed from any normal form game. In the end, we
recommend a model that achieved excellent performance across the board: a
linear weighting of features that requires the estimation of four weights. We
evaluated the effects of combining this new level-0 model with several
iterative models, and observed large improvements in the models' predictive
accuracies.

34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int