Lab Experiments are a Major Source of Knowledge in the Social Sciences

Laboratory experiments are a widely used methodology for advancing causal knowledge in the physical and life sciences. With the exception of psychology, the adoption of laboratory experiments has been much slower in the social sciences, although during the last two decades, the use of lab experiments has accelerated. Nonetheless, there remains considerable resistance among social scientists who argue that lab experiments lack ‘realism’ and ‘generalizability’. In this article we discuss the advantages and limitations of laboratory social science experiments by comparing them to research based on non-experimental data and to field experiments. We argue that many recent objections against lab experiments are misguided and that even more lab experiments should be conducted.laboratory experiments, field experiments, controlled variation

Lab Experiments are a Major Source of Knowledge in the Social Sciences

Laboratory experiments are a widely used methodology for advancing causal knowledge in the physical and life sciences. With the exception of psychology, the adoption of laboratory experiments has been much slower in the social sciences, although during the last two decades, the use of lab experiments has accelerated. Nonetheless, there remains considerable resistance among social scientists who argue that lab experiments lack “realism” and “generalizability”. In this article we discuss the advantages and limitations of laboratory social science experiments by comparing them to research based on non-experimental data and to field experiments. We argue that many recent objections against lab experiments are misguided and that even more lab experiments should be conducted.laboratory experiments, field experiments, controlled variation

Lab Experiments Are a Major Source of Knowledge in the Social Sciences

Laboratory experiments are a widely used methodology for advancing causal knowledge in the physical and life sciences. With the exception of psychology, the adoption of laboratory experiments has been much slower in the social sciences, although during the last two decades, the use of lab experiments has accelerated. Nonetheless, there remains considerable resistance among social scientists who argue that lab experiments lack "realism" and "generalizability". In this article we discuss the advantages and limitations of laboratory social science experiments by comparing them to research based on non-experimental data and to field experiments. We argue that many recent objections against lab experiments are misguided and that even more lab experiments should be conducted.laboratory experiments, field experiments, controlled variation

Asymptotic Conditional Distribution of Exceedance Counts: Fragility Index with Different Margins

Let $\bm X=(X_1,...,X_d)$ be a random vector, whose components are not necessarily independent nor are they required to have identical distribution functions $F_1,...,F_d$. Denote by $N_s$ the number of exceedances among $X_1,...,X_d$ above a high threshold $s$. The fragility index, defined by $FI=\lim_{s\nearrow}E(N_s\mid N_s>0)$ if this limit exists, measures the asymptotic stability of the stochastic system $\bm X$ as the threshold increases. The system is called stable if $FI=1$ and fragile otherwise. In this paper we show that the asymptotic conditional distribution of exceedance counts (ACDEC) $p_k=\lim_{s\nearrow}P(N_s=k\mid N_s>0)$, $1\le k\le d$, exists, if the copula of $\bm X$ is in the domain of attraction of a multivariate extreme value distribution, and if $\lim_{s\nearrow}(1-F_i(s))/(1-F_\kappa(s))=\gamma_i\in[0,\infty)$ exists for $1\le i\le d$ and some $\kappa\in{1,...,d}$. This enables the computation of the FI corresponding to $\bm X$ and of the extended FI as well as of the asymptotic distribution of the exceedance cluster length also in that case, where the components of $\bm X$ are not identically distributed

Strain localization in a shear transformation zone model for amorphous solids

We model a sheared disordered solid using the theory of Shear Transformation Zones (STZs). In this mean-field continuum model the density of zones is governed by an effective temperature that approaches a steady state value as energy is dissipated. We compare the STZ model to simulations by Shi, et al.(Phys. Rev. Lett. 98 185505 2007), finding that the model generates solutions that fit the data,exhibit strain localization, and capture important features of the localization process. We show that perturbations to the effective temperature grow due to an instability in the transient dynamics, but unstable systems do not always develop shear bands. Nonlinear energy dissipation processes interact with perturbation growth to determine whether a material exhibits strain localization. By estimating the effects of these interactions, we derive a criterion that determines which materials exhibit shear bands based on the initial conditions alone. We also show that the shear band width is not set by an inherent diffusion length scale but instead by a dynamical scale that depends on the imposed strain rate.Comment: 8 figures, references added, typos correcte