Punctuated Equilibrium Public Policy Theory

The punctuated equilibrium theory on public policy formulation is a useful tool in understanding the ways in which public institutions craft policy. The theory, developed by Frank Baumgartner and Bryan Jones in 1995, states policy changes inherently occur gradually. Factors including the polarization of political ideologies and cultural divides generally make policy formulation a slow, often stagnant process. However, a policy can change dramatically spurred by fundamental events that can motivate the public to pressure policymakers to implement a new policy. For example, the terrorist attacks of September 11, 2001 were a punctuated moment that resulted in dramatic changes our country’s homeland security and defense policies In this paper, we will examine three areas in which the concepts of punctuated equilibrium theory can be used to illustrate and understand how the United States implemented rapid policy changes in three areas: environmental, gun-control, and homeland security. Each policy field can be directly applied to the punctual equilibrium theory because of their nature of having long periods of policy stability which are punctuated by quick shifts in policy driven by short, but intense periods of instability and change

Computing Bayes Factors for Evidence-Accumulation Models Using Warp-III Bridge Sampling

Over the last decade, the Bayesian estimation of evidence-accumulation models has gainedpopularity, largely due to the advantages afforded by the Bayesian hierarchical framework.Despite recent advances in the Bayesian estimation of evidence-accumulation models,model comparison continues to rely on suboptimal procedures, such as posterior parameterinference and model selection criteria known to favor overly complex models. In this paperwe advocate model comparison for evidence-accumulation models based on the Bayesfactor obtained via Warp-III bridge sampling. We demonstrate, using the Linear BallisticAccumulator (LBA), that Warp-III sampling provides a powerful and flexible approachthat can be applied to both nested and non-nested model comparisons, even in complexand high-dimensional hierarchical instantiations of the LBA. We provide an easy-to-usesoftware implementation of the Warp-III sampler and outline a series of recommendationsaimed at facilitating the use of Warp-III sampling in practical applications

A cautionary note on evidence-accumulation models of response inhibition in the stop-signal paradigm

The stop-signal paradigm is a popular procedure to investigate responseinhibition–the ability to stop ongoing responses. It consists of a choice responsetime (RT) task that is occasionally interrupted by a stop stimulussignaling participants to withhold their response. Performance in the stopsignalparadigm is often formalized as race between a set of go runners triggeredby the choice stimulus and a stop runner triggered by the stop signal.We investigated whether evidence-accumulation processes, which have beenwidely used in choice RT analysis, can serve as the runners in the stop-signalrace model and support the estimation of psychologically meaningful parameters.We examined two types of the evidence-accumulation architectures:the racing Wald model (Logan, Van Zandt, Verbruggen, & Wagenmakers, 2014) and a novel proposal based on the Lognormal race (Heathcote & Love,2012). Using a series of simulation studies and fits to empirical data, wefound that these models are not measurement models in the sense that thedata-generating parameters cannot be recovered in realistic experimentaldesigns

A Bayesian approach for estimating the probability of trigger failures in the stop-signal paradigm

Response inhibition is frequently investigated using the stop-signal paradigm, where participants perform a two-choice response time task that is occasionally interrupted by a stop signal instructing them to withhold their response. Stop-signal performance is formalized as a race between a go and a stop process. If the go process wins, the response is executed; if the stop process wins, the response is inhibited. Successful inhibition requires fast stop responses and a high probability of triggering the stop process. Existing methods allow for the estimation of the latency of the stop response, but are unable to identify deficiencies in triggering the stop process. We introduce a Bayesian model that addresses this limitation and enables researchers to simultaneously estimate the probability of trigger failures and the entire distribution of stopping latencies. We demonstrate that trigger failures are clearly present in two previous studies, and that ignoring them distorts estimates of stopping latencies. The parameter estimation routine is implemented in the BEESTS software (Matzke et al., Front. Quantitative Psych. Measurement, 4, 918; 2013a) and is available at http://dora.erbe-matzke.com/software.html