2,274,509 research outputs found
Bidirectional branch and bound for controlled variable selection. Part III: local average loss minimization
The selection of controlled variables (CVs) from available measurements through
exhaustive search is computationally forbidding for large-scale processes. We
have recently proposed novel bidirectional branch and bound (B-3) approaches for
CV selection using the minimum singular value (MSV) rule and the local worst-
case loss criterion in the framework of self-optimizing control. However, the
MSV rule is approximate and worst-case scenario may not occur frequently in
practice. Thus, CV selection by minimizing local average loss can be deemed as
most reliable. In this work, the B-3 approach is extended to CV selection based
on local average loss metric. Lower bounds on local average loss and, fast
pruning and branching algorithms are derived for the efficient B-3 algorithm.
Random matrices and binary distillation column case study are used to
demonstrate the computational efficiency of the proposed method
Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails
We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.treatment effects, propensity score, bias, unconfoundedness, selection on unobservables
Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails
We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.Treatment Effects, Propensity Score, Bias, Unconfoundedness, Selection on Unobservables
Aspiration Dynamics of Multi-player Games in Finite Populations
Studying strategy update rules in the framework of evolutionary game theory,
one can differentiate between imitation processes and aspiration-driven
dynamics. In the former case, individuals imitate the strategy of a more
successful peer. In the latter case, individuals adjust their strategies based
on a comparison of their payoffs from the evolutionary game to a value they
aspire, called the level of aspiration. Unlike imitation processes of pairwise
comparison, aspiration-driven updates do not require additional information
about the strategic environment and can thus be interpreted as being more
spontaneous. Recent work has mainly focused on understanding how aspiration
dynamics alter the evolutionary outcome in structured populations. However, the
baseline case for understanding strategy selection is the well-mixed population
case, which is still lacking sufficient understanding. We explore how
aspiration-driven strategy-update dynamics under imperfect rationality
influence the average abundance of a strategy in multi-player evolutionary
games with two strategies. We analytically derive a condition under which a
strategy is more abundant than the other in the weak selection limiting case.
This approach has a long standing history in evolutionary game and is mostly
applied for its mathematical approachability. Hence, we also explore strong
selection numerically, which shows that our weak selection condition is a
robust predictor of the average abundance of a strategy. The condition turns
out to differ from that of a wide class of imitation dynamics, as long as the
game is not dyadic. Therefore a strategy favored under imitation dynamics can
be disfavored under aspiration dynamics. This does not require any population
structure thus highlights the intrinsic difference between imitation and
aspiration dynamics
Analytical Investigation of Innovation Dynamics Considering Stochasticity in the Evaluation of Fitness
We investigate a selection-mutation model for the dynamics of technological
innovation,a special case of reaction-diffusion equations. Although mutations
are assumed to increase the variety of technologies, not their average success
("fitness"), they are an essential prerequisite for innovation. Together with a
selection of above-average technologies due to imitation behavior, they are the
"driving force" for the continuous increase in fitness. We will give analytical
solutions for the probability distribution of technologies for special cases
and in the limit of large times.
The selection dynamics is modelled by a "proportional imitation" of better
technologies. However, the assessment of a technology's fitness may be
imperfect and, therefore, vary stochastically. We will derive conditions, under
which wrong assessment of fitness can accelerate the innovation dynamics, as it
has been found in some surprising numerical investigations.Comment: For related work see http://www.helbing.or
Ethnic enclaves and immigrant labour market outcomes: quasi-experimental evidence
This study investigates empirically how residence in ethnic enclaves affects labour
market outcomes of refugees. Self-selection into ethnic enclaves in terms of
unobservable characteristics is taken into account by exploitation of a Danish spatial
dispersal policy which randomly disperses new refugees across locations conditional
on six individual-specific characteristics.
The results show that refugees with unfavourable unobserved characteristics are
found to self-select into ethnic enclaves. Furthermore, taking account of negative
self-selection, a relative standard deviation increase in ethnic group size on average
increases the employment probability of refugees by 4 percentage points and
earnings by 21 percent. I argue that in case of heterogenous treatment effects, the
estimated effects are local average treatment effects
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