Hi Dr. Gehring: Thanks for the kind words and the question; it’s a good one. My instinct was that we got a lot of survey responses from academic researchers who, by the nature of their institutions, don’t often influence organizational decisions. However, this turns out not to be the case: The percent of respondents from academia and government indicating that ideas from the workshop would influence decisions in their organizations were 30 percent and 29 percent, respectively.

One potential issue is survey bias against responses from people in positions of power. We had quite a few directors and managers at the workshop, and while those people have the most direct ability to translate ideas from the workshop into organizational decisions, they tend to be very busy and perhaps unlikely to fill out a survey.

Another potential explanation is that there is simply a lot of inertia in organizations working on California water issues, and it is difficult to translate new ideas into organizational decisions. If this is the case, it suggests a different approach to incorporating better science into management decisions – we don’t (just) need information transfer, we need institutional reform. We plan to host similar workshops in subsequent years; in the future we should include survey questions that get at this distinction.

It’s worth noting that the trend you observed doesn’t seem to be driven by dissatisfaction with the workshop. There’s no apparent correlation between, e.g., ratings of how successful the workshop was at facilitating integration of knowledge or interest in attending subsequent workshops with agreement that workshop ideas will influence organizational decisions.

Again, thanks for the question. Since one of our major goals is to improve management planning, it’s important that we think about how we can make the workshop more influential in future years.

Thank you, Michael.
Catherine

Hello Dr. Lefticariu: Thank you for the question – answering it (more comprehensively than I can here, of course) will be an important step toward my dissertation. Social networks are often measured by survey prompts such as, “Please list up to 10 individuals with whom you have communicated about farm management.” Then one can examine the extent to which individuals that are socially connected share behavioral patterns. My ultimate goal is to simulate farmer decision making as it is influenced by the farmer’s environmental, economic, and social factors, and as it influences the environmental, economic, and social systems within which the farmer operates. I plan to do this using an approach called agent-based modeling, wherein farmers are each represented as individual “agents” within a simulation model that also represents the relevant hydrologic and economic systems. As a simple illustrative example, a farmer’s crop-choice decision could be modeled as selecting the option that maximizes their expected economic return, but where traditional economic models would assume the farmer has complete knowledge of the system, we could limit the farmer’s knowledge to the expected returns of crops that their social connections have grown in the previous three seasons. From there, I’m sure you can imagine myriad ways to incorporate social network effects that could make the model more realistic (subject to testing, of course). One approach I am particularly optimistic about is including a penalty term in the above-mentioned maximization function for uncertainty around the economic return, which would be reduced as more of a farmer’s network connections have selected that option (e.g., if no one you know has ever used drip irrigation, you’ll be quite uncertain about its results, so it would have to be potentially very profitable for you to adopt it. On the other hand, if a dozen of your farmer friends have used drip irrigation, you’ll have a pretty good idea of how profitable that will be, so you’ll be more likely to adopt it if it will increase your returns). That suggests an economic approach, but a social diffusion approach could be used alternatively or in concert, wherein as more of one’s social connections select option A, the more likely the individual is to do the same. Following a diffusion of innovation framework, heterogeneity could be added to such a system by varying the threshold fraction of one’s connections that have adopted a behavior required for the individual to adopt. There are many possibilities, and a big open question in this science is how to represent that network influence. In the end, I will likely compare models using different parameterizations to see which has the most fidelity to the system being studied. I hope I have answered your question, if not, or if you have others, please follow up!

Hi Dr. Yeakley – thanks for the question. The technique agent-based modeling allows representation in a simulation of individuals or organizations as objects that can influence and be influenced by their environments and other agents. In California, agriculture is the predominant use of water, so we’ll start with representation of farmers as agents in a hydrologic model, each coupled to a specific parcel of land. Candidate farmer decisions to be modeled include crop choice, irrigation method, and irrigation source (groundwater vs. surface water). There are many potential ways to parameterize the behavior of agents including simple heuristics, cognitively-based models, statistical parameterization, and the rational-actor model of economics. One approach I’m optimistic about is a bounded rationality design with Bayesian updating, building on the approach of Ng et al. in their 2011 paper, “An agent-based model of farmer decision-making and water quality impacts…”. They modeled farmer decisions as maximization of expected return subject to a penalty for uncertainty around that return. As more farmers in their system have, for example, grown a certain crop, the variance around other farmers’ expectations about the yield of that crop decreases, shrinking the uncertainty “penalty,” making planting of that crop (if it’s expected to be profitable) more likely. Among other things, I’m thinking of adding to this the constraint that farmers don’t incorporate knowledge from the experience of all farmers in the system equally – as Dr. Prokopy points out in our video, they are influenced by their social connections; they know more about and weigh more heavily evidence from their social connections. I’d like to simulate farmer-agents that are situated in a social network and refine their expectations about various choices based on the experiences of their social connections. This could enable the emergence of diffusion-of-innovation processes from individual-level characteristics of social connectedness, profit motivation, and openness to innovation.

Thanks for the question. We are currently synthesizing key outcomes of the workshop into a more formal document that will be shared with workshop participants as well as the broader community. In this process, we are building on the connections we made at the workshop through further conversations with policy makers and managers. Going forward, we will also continue to engage people around more specific topics related to our research. This will include invited seminar presentations as well as the continuation of the annual workshop. As we formalize our research questions, I know that I and others in my cohort are following up with individuals who might influence how we think about the problem and who might use our research. It was clear from the workshop that the integration between science and policy is a reciprocal process. Through the conversations between those doing the science and those developing policy from it, objectives can be defined and questions reframed. By developing these questions together, I may discover ways to adjust my research questions such that results directly inform decisions and policy makers and managers may find that there are new questions they should be addressing. This relates to your second question as well, in the sense that though policy makers do have many questions, particularly when it comes to specific short term decisions, it is through conversations like those at the workshop that questions are reshaped and questions to address longer term objectives are more clearly articulated.

Thank you! We enjoyed putting it on, learned a lot, and are looking forward to putting together an even better one next year!