Message from the Director, Mark Lubell
The mission of the Center for Environmental Policy and Behavior (CEPB) is scientific analysis of the interactions among policy institutions, human behavior, and political decisions in the context of environmental and natural resource conflicts. Through developing and testing theoretical models from social science, CEPB seeks to derive practical lessons that can be used to improve environmental policy.
There are many reasons to be dismayed about the outlook for environmental policy under the Trump administration. His potential appointees to the Environmental Protection Agency, and Departments of Agriculture, Interior, and Energy not exactly environmental advocates. These political appointees will lead efforts to roll back many of the environmental initiatives of the Obama administration, although they may encounter resistance from career civil servants in management positions. Trump does not recognize the validity of climate science, or even “science” writ large, despite substantial research about the economic costs resulting from human damage to the environment. Overall, the Trump administration offers a gloomy forecast that will once again force the environmental community to play political defense.
The question of being pro- or anti-reductionist came up briefly in a recent lab meeting. This is a re-hash of a piece I wrote a few years ago in response to a research funding allocation question that touches on that subject. It relates to a question that was being posed by the government Agriculture/Environment department which supported much of the work I did back then. The specific example is in the context of designing a science program to address a policy question, but I think the method may be useful at the start of the design process for any new program of research.
Last week, I gave an overview of bunch of tidyverse packages (tibble, dplyr, tidyr, ggplot, readr, purrr) to the Davis R-Users’ Group. Here is that talk (and since videos don’t display everywhere this blog is syndicated, here is the YouTube link).
I mention early in the talk that the
github_markdown specification in the YAML header produces a conveniently GitHub-renderable markdown file – here that is if you’d like to follow along, or you can download the rendered R Notebook (nb.html) file, which itself includes the R Markdown file (Awesome! In the upper right of the html file, click “Code” -> “Download Rmd”).
Ramiro Berardo and I recently published a new article on the structure of polycentric and complex governance systems for water management (sorry for the gated links…but see key figure inserted in this blog, where policy actors are circles, venues squares, and links represent participation). We have been working on this project for a number of years, driven by the reality that most environmental governance arrangements involve many different actors participating in multiple policy venues, and working on interrelated problems. Fortunately, veteran California environmental policy-maker Phil Isenberg was kind enough to provide a commentary on the article. Among Phil’s comments are, “For those of us with some responsibility for making decisions on water and the environment and hoping to 'do good'
Here is a video recording of my talk from useR! 2016 on teaching R. It’s nominally about teaching a lot of students in an intensive format, but I think almost everything translates to traditional classes. If for whatever reason this video isn’t working out for you, here is the source.
This talk was just one in a great session. I’d highly recommend:
Most researchers are misinterpreting geometrically weighted degree (GWD) estimates in exponential random graph models (ERGMs) of networks. By a 3:1 ratio papers cite positive estimates of GWD as indicative of a popularity or centralization force; in fact, positive estimates indicate dispersion of edges.
Here is a Shiny app that allows you to examine the effects of GWD parameter and decay-parameter values on network degree distributions. On the app’s other tabs, it provides some intuition on how the GWD statistic works and how GWD and GWESP – which is used to model triadic closure – are confounded.
I presented this research at the 2016 Political Networks conference. Check out the poster, which includes a literature review showing how prevalent this mistake is, by clicking on the image.
On May 9, 2016 the State Water Resources Control Board (SWRCB) announced new emergency water conservation regulations applicable to urban water suppliers throughout the state.
In a recent New York Times editorial, Charles Fishman argues “Water is Broken. Data Can Fix It.” He laments the dearth of water data in the United States, and suggests that increasing the collection and availability of water data will create a demand for additional information, change behavior, and ignite innovation. Mike Kiparsky and Joshua Viers reiterate this idea in the Los Angeles Times, in the context of needing better information for California water.
How can a simple game represent a complex social-ecological system? For the last few years, I have taught a graduate class on social-ecological systems (SES) that introduces SES concepts and frameworks along with delving into a number of related topics in environmental social science. A core activity of the class involves student groups choosing SES case studies, and applying the course topics from a particular week to the case study. Over the years, student groups have come up with creative participatory SES games as an alternative to top-down presentations. The Winter 2016 students took participatory games to the most advanced level yet—all four student groups created games representing their SES case studies.
We just released a policy brief with our initial analysis of the structure of Twitter networks centered on California agriculture. Starting with 153 users identified as relevant to California agriculture by the UC Division of Agriculture and Natural Resources, we traced the followers and followees of the initial group to identify approximately 59K Twitter users. The results clearly support the idea that social media outlets like Twitter can be a valuable aspect of strategic communications, education, and outreach about agriculture and the environment. Among the most interesting findings are:
1. The network is divided into 10 communities including climate, food, water, agriculture, plant sciences, politics, international development, viticulture, gardening, and animal welfare.