Canceling Science and Monetizing Outrage

If we maintain the fantasy of a puritan separation of science and business then innovation will dry up and die. There will be no one left to block and tackle for science or help us navigate the valley of death that lies between a scientific discovery and a cure, product, or better policy. The negative epistemic valence being cast by digital and mainstream media is polluting the commons of scientific communication, hindering the public's ability to distinguish fact from fiction. The implications for health, climate, democracy, and human welfare are tremendous.

Background

In a recent article in the New York Times the intersection between business and science are at the center of debate regarding ongoing climate research by Dr. Frank Mitloehner at UC Davis. This parallels a prior article from some years ago about  Dr. Kevin Folta and his work as it relates to agricultural biotechnology and science communication and outreach.

In the article, it seems to assert that Mitloehner's industry connections and collaboration are compromising his integrity and research as it relates to the relationship between livestock and GHG emissions.

Below are some of the most critical comments from the article:

“Industry funding does not necessarily compromise research, but it does inevitably have a slant on the directions with which you ask questions and the tendency to interpret those results in a way that may favor industry,” said Matthew Hayek, an assistant professor in environmental studies at New York University.

“Almost everything that I’ve seen from Dr. Mitloehner’s communications has downplayed every impact of livestock,” he said. “His communications are discordant from the scientific consensus, and the evidence that he has brought to bear against that consensus has not been, in my eyes, sufficient to challenge it.”

Communicating the Science

Assertions are made, but no evidence is offered in relation to how Mitloehner's research is compromised or in what ways his work contrasts with any scientific consensus. But it certainly puts his communications about his research on the chopping block. This is a big risk of doing science communication and outreach, as I have discussed before here.  In attempting to simplify complex scientific ideas for a broader audience, communicators are at risk for getting called out for any particular nuance they failed to include. It also creates enough space for any critic to write an entire thesis about why you are wrong. As I stated previously:

"Usually this is about how they didn't capture every particular nuance of the theory, failed to include a statement about certain critical assumptions, or over simplified the complex thing they were trying to explain in simple terms to begin with. This kind of negative social harassment seems to be par for the course when attempting to communicate on social media ... A culture that is toxic toward effective science communication becomes an impediment to science itself and leaves a void waiting be filled by science deniers, activists, policy makers, decision makers, and special interests."

One example called out in the NYT article was the production of a video called Rethinking Methane:

The article states: 

“The message of the five-minute video is that, because methane is a relatively short-lived greenhouse gas (once it’s in the atmosphere, it becomes less potent as the years go by), cattle would not cause additional warming as long as their numbers did not grow.”

“The argument leans on a method developed by scientists that aims to better account for the global-warming effects of short-lived greenhouse gases like methane. However, the use of that method by an industry “as a way of justifying high current emissions is very inappropriate” 

When considering sources of GHG emissions, understanding the way methane behaves is fundamental to understanding climate change, and personal and policy decisions related to mitigating future warming.  Understanding this can help direct attention to those areas where we can make the biggest difference in terms impacting climate change. As discussed in Allen et al. (2018):

"While shorter-term goals for emission rates of individual gases and broader metrics encompassing emissions’ co-impacts remain potentially useful in defining how cumulative contributions will be achieved, summarising commitments using a metric that accurately reflects their contributions to future warming would provide greater transparency in the implications of global climate agreements as well as enabling fairer and more effective design of domestic policies and measures."

But instead of diving into the meat (pun intended) of the science, the second statement about this video makes an assertion about using this science to justify high current emissions. 

Is that what Dr. Mitloehner is doing in his many communications, or is it actually the case that when we estimate the impact of climate change he thinks we should be using metrics that do a better job capturing the dynamics of different GHGs?  If his science really led him to dismiss the 'current high emissions' related to methane then why would he be spending time and energy researching and communicating about ways to reduce GHG emissions related to methane via feed additives and other management practices? 

And when we talk about current high emissions related to livestock what do we mean - compared to what?  The article states:

"scientific research has long shown that agriculture is also a major source of planet-warming emissions, ranking below the leading causes — the burning of coal, gas and oil — but still producing almost 15 percent of global emissions, the United Nations estimates."

That is a nice factoid, but it conflates all global emissions from agriculture with livestock emissions. It also makes kind of an ecological fallacy if we attribute that global number to the specific GHG emissions related to livestock of a specific country, particularly when the audience here is U.S. consumers who mostly eat beef produced in the U.S. (where in fact the the contribution to total global GHG emissions is less than 1/2 of 1%.)

The fact about global numbers is relevant to Mitloehner's work only in the sense that his research could have much greater impact in developing countries where GHG emissions may be 10X greater (EPA GHG Emissions Inventory, Rotz et al, 2018). As stated in a recent article in Foreign Policy: 

"Generalizations about animal agriculture hide great regional differences and often lead to diet guidelines promoting shifts away from animal products that are not feasible for the world’s poor....A nuanced approach to livestock was endorsed in the latest mitigation report of the U.N. Intergovernmental Panel on Climate Change (IPCC).....there is great room for improvement in the efficiency of livestock production systems across developing countries" 

But these nuances are lost in the NYT article along with recognition that there are multiple margins to consider when thinking about the tradeoffs related to food production and consumption. Policy should consider the numerous choices consumers and producers make in a modern and global economy in relation to nutrition, energy, and climate.  

Parallels with the Past

When reflecting on this NYT article and the prior article focused on Dr. Kevin Folta I see at least three parallels:

1) An appeal to the nirvana fallacy of a perfect separation between science and business. While this is not explicitly stated, both stories paint a picture of malfeasance and industry influence connected with the work of these scientists, without providing evidence that that their research or findings are biased or conflict with any major consensus. They simply imply that any industry connection is questionable, Guilt by association alone.

2) Establishing some theatre of doubt around the integrity and character of these scientists in the mind of readers, the next step involves a kind of ad hominem reasoning suggesting that because of these industry connections and questionable integrity of the researchers, anything they claim must be false or misleading or contradictory to the mainstream scientific consensus.

Having established the first two parallels, the public is then set up to make a third mistake in reasoning:

3) Argument by intimidation. The implication here is that anyone that references or leans on the work of these scientists must also have questionable integrity or character. This can be invoked as a way to bypass debate and avoid discussing the actual science or evidence supporting the claims one may be making. I'm not saying that the NYT article does this explicitly, but this article pollutes the science communication environment in a way that makes this more likely to happen..

This leads me to ask - why would mainstream media follow this kind of recipe when producing stories?

Changing Business Models for Modern Media

In Jonathan Rauch's book, the Constitution of Knowledge, he discusses how in the old days of print media economies of scale supported the production of real news or reality based content. But new business models have been built on information, not knowledge and are geared toward monetizing eyeballs and clicks. This new business model favors "professionals in the arts of manipulative outrage: the kinds of actors who were more skilled at capturing attention not persuasion and who were more interested in dissemination than communication."

Rauch observes: "By the early 2020s high quality news was struggling to stay in business, while opinion, outrage, derivative boilerplate, and digital exhaust (personal data generated by internet users) enjoyed a thriving commercial market."

Quoting one digital media pundit: "you can't sell news for what it costs to make."

As mainstream media has adopted social and digital media strategies it may not be surprising to see patterns like those above emerge.

 In 2020 former President Barak Obama said in The Atlantic: 

"if we do not have the capacity to distinguish what's true from what's false, then by definition the marketplace of ideas doesn't work. And by definition our democracy doesn't work. We are entering into an epistemological crisis." 

Communicating science is challenging enough. The battle with misinformation and disinformation did not begin or end with the COVID pandemic. It doesn't help when major media outlets would rather cash in on eyeballs and outrage, rather than communicate science.

Related Readings and Resources

Allen, M.R., Shine, K.P., Fuglestvedt, J.S. et al. A solution to the misrepresentations of CO2-equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Clim Atmos Sci 1, 16 (2018) doi:10.1038/s41612-018-0026-8

C. Alan Rotz et al. Environmental footprints of beef cattle production in the United States, Agricultural Systems (2018). DOI: 10.1016/j.agsy.2018.11.005 

Facts, Figures, or Fiction: Unwarranted Criticisms of the Biden Administration's Failure to Target Methane Emissions from Livestock. https://ageconomist.blogspot.com/2021/12/facts-figures-or-fiction-unfair.html  

The Ethics of Dietary Nudges and Behavior Change Focused on Climate and Sustainability. https://ageconomist.blogspot.com/2022/10/the-ethics-of-dietary-nudges-and.html

Will Eating Less U.S. Beef Save the Rainforests? http://realclearagriculture.blogspot.com/2020/01/will-eating-less-us-beef-save.html

The Economics of Innovation in Biopharma

This podcast touches on the lack of innovation in pharma and criticism about outsourcing innovation. Do these criticisms ignore recent technological advances in biotech (and the convergence of AI and genomics) that have reduced the minimum efficient scale in drug discovery creating new opportunities for startups, small firms, and scientist entrepreneurs? When we think of therapeutics as dispensing knowledge packed into a capsule or syringe, knowledge that has properties of both a private and public good (i.e. non-rival and partially excludable) scientist entrepreneurs are better incentivized and able to capture greater value from their discoveries in a venture capital funded startup environment than a larger institution like pharmaceutical companies or universities (even with Bayh-Dole Act). Drug discovery is risky, but by combining option value and discovery of new information with staged investment VC firms can discover positive NPV projects that would otherwise be rejected under conventional financing models. The combination of technological change, the economics of knowledge, and venture capital seems to reduce the comparative advantage of innovating 'in-house.' Maybe it is the case that large pharmaceutical firms have more of a comparative advantage navigating the valley of death that lies between a discovery and a cure by focusing on the regulatory approvals and marketing efforts necessary to deliver those products than they have in drug discovery?

The Convergence of AI, Life Sciences, and Healthcare

Several years ago I was writing about the convergence of AI and genomics in agriculture:

"The disruptions of new technology, big data and genomics (applications like FieldScripts, ACRES, MyJohnDeere or the new concept Kinze planters that switch hybrids on the go etc.) will require the market to continue to offer a range of choices in seeds and genetics to tailor to each producer's circumstances of time and place." (1)

We have also seen a similar convergence in healthcare:

"A series of breakthroughs in medical science and information technology are triggering a convergence between the healthcare industry and the life sciences industry, a convergence that will quickly lead to more intimate—and interactive—relationships among people, their doctors, and biopharmaceutical companies."  (2)

This excellent segment on WBUR just a few years later picks up on the same themes:

Nobel Laureate and MIT Institute professor Phil Sharp has an even broader vision of this convergence: It’s not just computer science and biology that are converging, but engineering, physics, material science and agriculture too, he says.

“Life science is part of all of those processes and bringing physicists and engineering and information technology together to integrate life science with the translation to solving those problems is what convergence is about,” Sharp says. “It'll be decades of exciting science and exciting technology.” (3)

There are a number of parallels I want to discuss below including outcomes and value based pricing, precision medicine and precision agriculture, venture capital and digital solutions, and how these trends are leading to products and solutions that can address some of society's biggest problems like healthcare quality and cost, social determinants of health, and climate change.

Outcomes and Value Based Pricing

Due to this convergence, better data and technology are creating new opportunities. Health insurance companies, healthcare providers, and seed companies are entering into value based contracts where payments are based on outcomes and quality. 

In healthcare:

"By leveraging appropriate software tools, big data is informing the movement toward value-based healthcare and is opening the door to remarkable advancements, even while reducing costs. " (4)

"Value-based healthcare is a healthcare delivery model in which providers, including hospitals and physicians, are paid based on patient health outcomes. Under value-based care agreements, providers are rewarded for helping patients improve their health, reduce the effects and incidence of chronic disease, and live healthier lives in an evidence-based way." (5)

(See below or  https://healthinformatics.uic.edu/blog/shift-from-volume-based-care-to-value-based-care/ for an excellent infographic explaining this promising shift in healthcare)

In food and agriculture we are seeing risk sharing and outcomes based pricing contracts as well:

"...executives are touting their new pricing model, outcome-based pricing, as the potential pricing paradigm of the future. The model involves Bayer setting an expected yield outcome for a product or seed, based on a farm's data and history stored on the company's digital ag platform, FieldView, as well as the company's own research on their products. If a farmer's final yield falls below that expected value, the company will rebate a certain portion of the original price of the product. If the yield instead surpasses the initial set value, the farmer shares a pre-agreed portion of that additional income with the company." (6)

Precision Medicine and Precision Agriculture

Instead of one size fits all best practices for seed, pest management, tillage, and nutrient management recommendations driven by research from university and industry trials, growers can get individually customized prescriptions, not just at the farm or field level, but within field and moving closer and closer to the row foot level for some decisions. The combination of advanced genomics with big data generated from precision agricultural applications (remote sensing, IoT, automated steering, GPS/GIS) makes one size fits all obsolete. 

As I quoted previously: 

"That's also why the market has driven companies to treat hybrid selection like a 'big data' problem and they are developing multivariate recommender systems as tools to assist in this (like ACRES and FieldScripts). The market's response to each individual producer's unique circumstances of time and place also ensures continued diversity of crop genetics planted. There are numerous margins that growers look at when optimizing their seed choices and it will require a number of firms and seed choices to meet these needs as the industry's focus moves from the farm and field level to the data gathered by the row foot with each pass over the field." (1)

Similarly, in healthcare, the golden age of medicine driven by the 'omics' revolution and big data will allow us to move away from one size fits all generalizations of research and medicine allowing us to "tailor medical treatment to the specific characteristics of each patient involving the ability to classify individuals into subpopulations that are uniquely susceptible to a specific treatment, sparing expense and side effects and is derived from doubts on the results of subgroup analyses and on non responders in clinical trials" (7)

"Health systems will have to go rapidly from a one-size-fits-all model of treatment to a more customized model, which still uses mass-manufactured but where treatments are selected for patients based on specific biomarkers," Joshi said. "But we can now see the next advance in personalized medicine potentially going even further, something much more personalized, like a tailor-made suit...."Big data and advances in our understanding of genomics are providing us with the footholds into establishing and understanding, for the first time ever, the causal genetic factors that help us manage that golden triangle of treatment: the right target, the right chemistry, and the right patient." (2)

Venture Capital and Digital Platforms and Solutions

Monsanto's (now Bayer Crop Science) acquisition of The Climate Corporation occurred about the same time I was penning my first post on this convergence, and was the first major move in industry that solidified these potential synergies in my mind at least. This convergence has drawn the interest and has been fueled by a number of startups and venture capital firms. Farmer's Business Network (FBN) seems to be positioning itself as a disruptor, like the Amazon of agribusiness providing a platform that includes everything from purchasing inputs, crop analytics, finance and marketing, and more direct access to genetics. In the livestock space, companies like AAD (Advanced Animal Diagnostics) and Connecterra are building tools and services analogous to a Fitbit for cows. Body Surface Translations (BST) is a company whose image processing technology has targeted both problems in animal and human health.  Tim Hammerich (the Future of Agriculture) and Sarah Nolet (AgTech So What?, AgThentic, Tenacious Ventures) have weekly discussions with innovators pioneering new solutions in this space covering a range of topics including automated irrigations systems, blockchain, regenerative agriculture, carbon sequestration and a range of companies from startups to larger players including Wal-Mart and Coca-Cola. Where Food Comes From is leveraging QR codes and mobile technology paired with their source verification processes to connect consumers to information about the people and processes behind the food they consume.  IN10T is a digitally powered data driven company helping bridge the gaps between innovations and real world application of these technologies. Venture capital firm Foresite Capital even leverages data science to drive their investment strategy in therapeutics, diagnostics, and devices. This includes digital health apps like mindstrong which is leveraging AI for better diagnosis, monitoring, and treatment of behavioral health conditions and everlywell focused on actionable healthcare diagnostics and health engagement. Evidation is a company that leverages data from digital devices and sensors capturing, quantifying, and analyzing behavior, or mapping the 'behaviorome' in the context of human health (8). This is just a tiny survey of companies and products that I have encountered in just the last few years.

Addressing Society's Bigger Problems

This convergence is allowing us to address problems in healthcare like quality, cost, access and health equity. When it comes to the food we eat, AI, technology, and genomics is providing us the tools to combat issues like climate change, water quality, nutrition, safety, equity, and access. 

It's obvious when you look at the big picture, this convergence is leading to progress that is both complimentary and synergistic across a range of industries related to food and healthcare. Better food and a healthier environment and planet  led to better health outcomes. Healthcare payers and providers are realizing the importance of these issues in healthcare. Each is separately addressing key social determinants of health in ways that were not possible before:

"During the past several decades, it has become increasingly apparent that a person’s “health” is influenced by many more factors than health care alone. These other determinants are defined by the conditions and environment in which people are born, grow, live, work, and age, reaching beyond just what the delivery of acute care services can influence. These “social determinants of health” result in billions of dollars of additional costs annually. By working to mitigate the negative impacts of these factors, significant benefits can be achieved that improve both access and outcomes for individuals and lower overall costs." (9)

As I stated several years ago:

"as big data drives more diversity into every seed planted in every acre across every field, we may possibly begin to mitigate some of the risks and concerns traditionally associated with monoculture. So it is true, when you look across row after row and see only corn, you might technically call it 'monoculture' but it's not your grandparent's monoculture." 

As a result of the convergence of AI and life sciences, it's not your grandparent's healthcare either. 

References and Related Readings:

(1) Monoculture vs. the Convergence of Big Data and Genomics. Matt Bogard. October 13, 2017. https://www.linkedin.com/pulse/monoculture-vs-convergence-big-data-genomics-matt-bogard/ (previously published as: Big Data + Genomics != Your Grandparent's Monoculture. Economic Sense. December 22, 2014. http://ageconomist.blogspot.com/2014/12/big-data-genomics-your-grandparents.html

(2) Big Data Gets Personal as Healthcare and Life Sciences Converge. By Bob Evans, Senior Vice President, Oracle.  https://www.oracle.com/industries/oracle-voice/big-data-gets-personal.html

(3) Next Chapter For Biotech? Many Say 'Convergence' With Data Science. WBUR. NPR. Bioboom June 8, 2018. https://wbur.fm/2MaaMkA

(4) Healthcare Big Data and the Promise of Value-Based Car. NEJM Catalyst. Brief Article. Jan 1, 2018

(5) What Is Value-Based Healthcare?. NEJM Catalyst. Brief Article. Jan 1, 2017

(6) Q&A With Bayer on Outcome-Based Pricing. By Emily Unglesbee. DTN Progressive Farmer. 10/2/2019 

(7) Capurso L. Evidence-based medicine vs medicina personalizzata [Evidence-based medicine vs personalized medicine.]. Recenti Prog Med. 2018 Jan;109(1):10-14. Italian. doi: 10.1701/2848.28748. PMID: 29451516. 

(8) Why Foresite Capital is Betting Big on the Convergence of AI and Biotech. August 23, 2018. https://soundcloud.com/levine-media-group/why-foresite-capital-is-betting-big-on-the-convergence-of-ai-and-biotech   Check out their current portfolio of investments: https://www.foresitecapital.com/portfolio/ 

(9) Beyond the Boundaries of Health Care: Addressing Social Issues https://www.ahip.org/beyond-the-boundaries-of-health-care-addressing-social-issues/ 

Related: 

What does the farmer say...about seed choices? (Channeling Hayek) http://ageconomist.blogspot.com/2013/12/what-does-farmer-say-about-seed-choices.html 

Big Data: Causality and Local Expertise Are Key in Agronomic Applications. http://econometricsense.blogspot.com/2014/05/big-data-think-global-act-local-when-it.html

Modern Sustainable Agriculture Annotated Bibliography. http://ageconomist.blogspot.com/2011/02/modern-sustainable-agriculture.html

University of Illinois at Chicago

Using Social Network Analysis to Understand the Influence of Social Harassment Costs and Preferences Toward Biotechnology

In a previous discussion I described how social harassment costs (Borland and Pulsinelli, 1989) might explain why some consumers could hold seemingly contradictory views about science (i.e. accepting certain scientific views related to global warming but rejecting other scientific views related to genetically modified foods). 

In my graduate school research I hypothesized that consumers adopt a worldview v (regarding climate change, food preferences, religious beliefs, public policy, etc.) that gives them the greatest level of utility seemingly invariant to evidence supporting some alternative worldview v'.

U(v) > U(v')  (1)

One way to to explain this would be to model utility as a function of social harassment  'c'. 

U(v, c) > U(v', c)  (2)

for c > k

U(v, c) < U(v', c)  (3)

for c < k

In this formulation social harassment provides disutility, and would enter the utility function as a negative term. If social harassment is great enough to exceed some threshold 'k', consumers with preferences like those above may choose to ignore scientific evidence that lowers utility by conflicting with their vision or the vision of their peers.  The level of 'k' may vary depending on the consumers sensitivity to social pressure.

Some of the implications of this model were that consumers might increase utility and reduce social harassment by avoiding information that conflicts with their world views, they might also seek information that supports utility maximizing views regardless of weight of evidence. 

This also seemed to align with a number of ideas supported by findings from behavioral  and public choice economics (Caplan, 2007; Kahneman, 2011). For example the idea that beliefs that are irrational from the standpoint of truth-seeking are rational from the standpoint of utility maximization (Caplan, 2007).

In graduate school I attempted to investigate this empirically by developing a survey instrument to measure preferences toward genetically modified foods as well as attitudes toward abortion, climate change, embryonic stem cell research, animal welfare as well as political ideology, education levels, and science knowledge. I found that respondents with a positive view of embryonic stem cell research and those that were more concerned about the impacts of climate change were less likely to accept the safety of genetically modified foods. This is in spite of evidence of the safety of biotechnology or its potential for mitigating the impacts of climate change. However, the sample size was very small and as noted elsewhere a better instrument and structural equation modeling approach might offer a much richer and more rigorous understanding of the latent factors shaping consumer perceptions.

Additionally, the behavioral theoretical utility model above is very general. While this model's predictions could be loosely supported by the empirical work, many untested assumptions remain. For instance, the level of social harassment 'c' and the threshold 'k'. These are abstract latent factors hard to estimate and validate empirically. 

However, if we think of social harassment being a function of our exposure to media, social media, and peers, we can begin to frame up an analytical strategy for better understanding these phenomena in the context of social network analysis (SNA). For example, assume two actors, 'A' and 'B' who have preferences similar to (2) and (3) above. And assume a simple network of connections with peers as depicted below:

Each node (depicted as black, white or grey dots above) represents a peer's sentiment toward genetically modified foods. For subject A, strongly influenced by peers with negative sentiments, we might hypothesize that the social harassment costs associated with believing in the safety of biotech crops could be high even in the face of strong scientific evidence (which they may not be aware of, discount highly, or avoid in order to maximize utility). For subject B, social harassment costs in relation to these beliefs might be much lower and likely be imposed rarely by a few peripheral connections. This is just a toy example, but this framework helps motivate a number of questions:

• How exactly should these networks be defined and constructed to properly frame the question/hypothesis I have? Who/what entities should each node represent (people, media outlets, websites, celebrities, scientists, etc.)?
• Connections between nodes are referred to as edges and represent pathways through which information and social harassment costs might flow - should different edges be given different weights as a function of the entity represented by each node? Are there interactions between the type of node and the type of information flowing from it? 
• Is there any correlation between network metrics (i.e. degree centrality, eigenvector centrality) and influence on preferences/perceptions? 
• What can we learn from previous research in SNA in the area of viral marketing? Are there key nodes that can be influenced? 
• What role does network architecture play in information diffusion, influence, and ultimately the level of social harassment costs of a given node (ultimately this is what I would want to quantify to empirically support the theoretical model above)?
• Are there causal inferential approaches with the necessary identification properties allowing us to interpret these effects causally? (see perhaps Tchetgen et al., 2020)

Applications could extend beyond perceptions of genetically modified foods to include climate change, food preferences, religious beliefs, or vaccines. Johnson et al.(2020) has made a lot of progress using a similar framework to study the spread of disinformation across social networks as it relates to attitudes toward vaccination:

"Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change and highlight the key role of network cluster dynamics in multi-species ecologies."

A recent discussion of this paper can be found via the Data Skeptic podcast: https://podcasts.apple.com/sg/podcast/the-spread-of-misinformation-online/id890348705?i=1000491199543 

References:

Borland,Melvin V. and Robert W. Pulsinelli. Household Commodity Production and Social Harassment Costs.Southern Economic Journal. Vol. 56, No. 2 (Oct., 1989), pp. 291-301

The Myth of the Rational Voter: Why Democracies Choose Bad Policies. Bryan Caplan. Princeton University Press. 2007

Johnson, N.F., Velásquez, N., Restrepo, N.J. et al. The online competition between pro- and anti-vaccination views. Nature 582, 230–233 (2020). https://doi.org/10.1038/s41586-020-2281-1

Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.

Eric J. Tchetgen Tchetgen, Isabel R. Fulcher & Ilya Shpitser (2020) Auto-G-Computation of Causal Effects on a Network, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1811098

SNA and Related Posts at EconometricSense:

Perceptions of GMO Foods: A Hypothetical Application of SEM

An Introduction to Social Network Analysis with R and NetDraw

GMM, Endogeneity, SNA, Viral Marketing, and Causal Inference

SNA & Learning Communities

Using SNA in Predictive Modeling

The Robustness of SNA Metrics

All SNA Posts at EconometricSense

Related Posts and Background at EconomicSense

Consumer Perceptions of Biotechnology: The Role of Information and Social Harassment Costs

Fat Tails, the Precautionary Principle, and GMOs

Defining Consensus Regarding the Safety of Genetically Modified Foods 

Comments of Rule for Rules on Gene Editing Technology

Rational Irrationality and Satter's Hierarchy of Food Needs

The 'Free From' Nash Equilibrium

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Polarization of Controversial Science and Limitations of Science Literacy

Voter Irrationality and Systematic Bias: Applications in Food and Biotechnology

Left vs Right Science vs Risk vs Propensity to Regulate

Consumer Perceptions of Biotechnology: The Role of Information and Social Harassment Costs

Agricultural biotechnology offers tremendous benefits to farmers and to society as it provides tools for mitigation of a number of environmental externalities related to water quality and food safety (USDA, 2000; Munkvold, 1999). However, perceptions of the safety of recombinant DNA technology (a.k.a. genetically engineered foods) on the part of consumers can shape the policy environment in ways that may inhibit expanded use of biotech traits in agriculture.

As a graduate student I found it particularly interesting that some consumers had strongly held science based views related to climate change, but might at the same time have views related to the safety of genetically modified foods that were at the time inconsistent with the larger scientific community. What could explain this?

In my work I hypothesized that consumers adopt a worldview v  (regarding climate change, food preferences, religious beliefs, public policy, etc.) that gives them the greatest level of utility.

U(v) > U(v')  (1)

One way to to explain this would be to model utility as a function of  social harassment  'c'. 

U(v,c) > U(v',c)  (2)

for c > k

U(v,c) < U(v',c)  (3)

for c < k

In this formulation social harassment provides disutility, and would enter the utility function as a negative term (I later found out you could alternatively model this similarly introducing a 'bliss' point in a utility model such that consumers might obtain utility from holding a certain viewpoint up to some level of saturation beyond which disutility sets in).

If social harassment is great enough to exceed some threshold 'k', consumers with preferences like those above may choose to ignore scientific evidence that lowers utility by conflicting with their vision or the vision of their peers.  The level of 'k' may vary depending on the consumers sensitivity to social pressure.

Those that are sensitive to social pressure and whose preferences are impacted strongly by the veracity of a particular vision may be resistant to conflicting evidence. 

If they were to accept the alternative (perhaps scientifically supported) viewpoint v*, and peers find these views distasteful, social harassment would lower utility. This could give the appearance of holding conflicting views related to scientific issues. An example would be accepting scientific consensus in some areas like evolution (where social harassment may be lower) but rejecting it in other areas like the safety or benefits of genetically engineered (GE) foods (where social harassment  may be higher in many circles). 

What do I mean by 'social harassment' and how might this relate to preferences toward genetically engineered foods? We might view them as a form of peer pressure, political correctness, or social norming. Consumers may choose a certain worldview (or express it through consumption patterns signaling their social viewpoints) based on their desire to be accepted by others. As a result they may discard any conflicting information or evidence and maximize utility by holding onto their world view 'v.'

U(v,c) > U(v',c) (2)

for c > k

I attempted to explore this theory empirically leveraging a data set containing demographics and survey responses related to political and religious views, food consumption preferences (organic/natural etc.), attitudes toward animal welfare, views on other scientific advances like stem cell research, scientific literacy, and attitudes toward genetically engineered foods. 

I found that respondents with a positive view of embryonic stem cell research and those that were more concerned about the impacts of climate change were less likely to accept the safety of genetically modified foods. This is in spite of evidence of the safety of biotechnology or its potential for mitigating the impacts of climate change. My theory of social harassment would be consistent with those empirical findings. (much more advanced work has been done in the last 15 years - see links and references below)

While I was aware at the time of previous work empirically estimating consumer attitudes toward genetically engineered foods (see references below) I was not aware of other related work in behavioral and public choice economics.

In retrospect, this not so different from the concept of 'rational irrationality' discussed in Bryan Caplan's 'The Myth of the Rational Voter':

"...people have preferences over beliefs. Letting emotions or ideology corrupt our thinking is an easy way to satisfy such preferences...Worldviews are more a mental security blanket than a serious effort to understand the world."

This means that:

"Beliefs that are irrational from the standpoint of truth-seeking are rational from the standpoint of utility maximization."

And in application:

"Support for counterproductive policies and mistaken beliefs about how the world works normally come as a package. Rational irrationality emphasizes this link."

From Kahneman's Thinking Fast and Slow:

"emotional attitude drives beliefs about benefits and risks and dominates conclusions over arguments."

Borland and Pulsinelli's Social Harassment Costs and Abatement

My idea of social harassment was inspired by Borland and Pulsinelli's work, although their formulation was in the context of household production (inspired by Gary Becker, 1965) with social harassment built into a budget constraint and utility maximization framework. Their discussion of social harassment costs as 'guilt trips' for driving gas guzzlers in the face of shortages and price controls was the motivating example for my thinking. 

A key idea from their theory is the concept of purchasing social abatement. Society will permit individuals to purchase goods or services that it finds distasteful if they also purchase abatement for that good.

While their paper predates contemporary notions of carbon offsets, the idea of hollywood stars or politicians buying carbon offsets to avoid social harassment from peers because of their jet setting lifestyles could be one application of social abatement. It follows from their logic that there is a market for goods that can be purchased for the purpose of social abatement. 

Perhaps some goods are 'bundled' with social abatement. For instance, a consumer that believes that beef consumption contributes excessively to climate change might be willing to buy beef if it has socially abating attributes like being grass fed, organic, or hormone free. Never mind that the carbon footprint of U.S. beef represents less that .5% of global greenhouse emissions and 3% of total U.S. GHG emissions, or the fact that these products may not actually lower beef's carbon footprint (and could possibly increase it - see also Rotz et al 2018). Another example, parents might be OK with a sugary cereal or drink if it is at least made with non-GMO ingredients (where the social points gained from being 'non-GMO' outweighs or abates social points lost from being a bad parent giving their kids sugary foods)

Social harassment can be explicit, in terms of negative comments from friends or colleagues, or they can simply be internal perceptions. But either way the purchase of socially abating goods or goods with socially abating characteristics could be explained this way. And food marketers are capitalizing on that in various ways in the form of free-from food labeling among other things.

Both my formulation and Borland and Pulsinelli's concept of social harassment costs would fit these trends in food preferences. My perspective differs in that social harassment impacts the views we adopt and the role of information in changing or shaping those views. These views either translate directly to consumption choices or indirectly as a means of signaling our views on food systems etc. In this formulation, consumer views are invariant to new information if it conflicts with their adopted view, because if others found out they had strayed in thought or conviction, they would incur social harassment disutility. So we might adopt a worldview that is unsupported by the wealth of scientific evidence. Purchases that signal socially desirable preferences help guarantee the higher level of utility and lower levels of social harassment. For those with the most strongly held views, they may evangelize others to adopt them (imposing additional social harassment on others). This signals that not only do I conform to the socially acceptable worldview, but I'm practicing my religion at the highest level.

Additional Implications

Social harassment may also be interacting with social media by making us aware of socially acceptable and unacceptable behaviors. This plays a role in influencing our worldviews as well as providing a platform for signaling our world views. This might explain the proliferation of increased attention paid to food in the last decade. 

While consumers might increase utility and reduce social harassment by avoiding information that conflicts with their world views, they might also seek information that supports utility maximizing views regardless of weight of evidence. 

In their paper "Monetizing disinformation in the attention economy: The case of genetically modified organisms (GMOs)" Ryan, Schaul, Butner and Swarthout provide an in depth background on the attention economy, disinformation, the role of the media and marketing as well as socioeconomic impacts. They articulate how how rent seekers and special interests are able to use disinformation in a way to create and economize on misleading but coherent stories with externalities impacting business, public policy, technology adoption, and health. These costs, when quantified can be substantial and should not be ignored:

"Less visible costs are diminished confidence in science, and the loss of important innovations and foregone innovation capacities"

Merchants of misinformation and disinformation can exploit consumers (and voters) with preferences sensitive to social harassment. 

Conclusion

The idea that facts alone often fail to change consumers minds is not novel. And the concept I leveraged related to social harassment and social harassment costs might not be so different from ideas related to social norming or social proof. However, I think it is a useful exercise to think through the implications of different formulations of these concepts because they can help us better understand the role of science and evidence in consumer perceptions and decision making. Better understanding may improve science communication. This could have implications for climate change, food sustainability, as well as vaccines and other impacts on public health.

Related Reading:

Perceptions of GMO Foods: A Hypothetical Application of SEM

Fat Tails, The Precautionary Principle, and GMOs

Defining Consensus Regarding the Safety of Genetically Modified Foods

Rational Irrationality and Satter's Hierarchy of Food Needs

The 'Free From' Nash Equilibrium

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Polarization of Controversial Science and Limitations of Science Literacy

Voter Irrationality and Systematic Bias: Applications in Food and Biotechnology

Left vs Right Science vs Risk vs Propensity to Regulate

Additional References:

Abdulkadri, Abdullahi O, Simmone Pinnock, and Paula Tennant. " Public Perception of Genetic Engineering and the Choice to Purchase Genetically Modified Food." Paper presented at the American Agricultural EconomicsAssociation Annual Meeting. 2004

Adulaja, Adesoji, et al. "Nutritional Benefits and Consumer Willingness to Buy Genetically Modified Foods." Journal of Food Distribution Research . Volume 34, Number 1. 2003 p. 24-29.

Baker, Gregory A. "Consumer Response to Genetically Modified Foods: Market Segment Analysis and Implications for Producers and Policy Makers." Journal of Agricultural and Resource Economics" Vol 26, No. 2. 2001. p.387-403.

Becker, G.S. (1965). ‘A theory of the allocation of time’, ECONOMIC JOURNAL, vol. 75(299), pp. 493–517

*Borland,Melvin V. and Robert W. Pulsinelli. Household Commodity Production and Social Harassment Costs.Southern Economic Journal. Vol. 56, No. 2 (Oct., 1989), pp. 291-301

Camille D. Ryan, Andrew J. Schaul, Ryan Butner, John T. Swarthout, Monetizing disinformation in the attention economy: The case of genetically modified organisms (GMOs), European Management Journal, Volume 38, Issue 1, 2020, Pages 7-18, ISSN 0263-2373

The Myth of the Rational Voter: Why Democracies Choose Bad Policies. Bryan Caplan. Princeton University Press. 2007

Chiappori, P.‐A. and Lewbel, A. (2015), Gary Becker's A Theory of the Allocation of Time. Econ J, 125: 410-442. doi:10.1111/ecoj.12157

Russell Golman, David Hagmann, George Loewenstein. Information Avoidance. Journal of Economic Literature, 2017; 55 (1): 96 DOI: 10.1257/jel.20151245

Hine, Susan and Maria Loureiro. "Understanding Consumers' Perceptions Toward Biotechnology and Labeling."Paper presented at the American Agricultural Economics Association Annual Meeting . 2002.

Jones, Gerald M., Anya McGuirk and Warren Preston. "Introducing Foods Using Biotechnology: The Case of Bovine Somatotropin." Southern Journal of Agricultural Economics. Vol 24, No. 1 1992. p 209-223

Lacey Wilson, Jayson L. Lusk,Consumer willingness to pay for redundant food labels,Food Policy, 2020,101938, ISSN 0306-9192,

Jayson L. Lusk, Brandon R. McFadden, Norbert Wilson, Do consumers care how a genetically engineered food was created or who created it?,Food Policy,Volume 78,2018,Pages 81-90,ISSN 0306-9192

Nicholas Kalaitzandonakes, Jayson Lusk, Alexandre Magnier, The price of non-genetically modified (non-GM) food,Food Policy,Volume 78,2018,Pages 38-50,ISSN 0306-9192

B. R. McFadden, J. L. Lusk. What consumers dont know about genetically modified food, and how that affects beliefs. The FASEB Journal, 2016; DOI: 10.1096/fj.201600598

National Academies of Sciences, Engineering, and Medicine. 2016. Genetically Engineered Crops: Experiences and Prospects. Washington, DC: The National Academies Press. https://doi.org/10.17226/23395.

C. Alan Rotz et al. Environmental footprints of beef cattle production in the United States, Agricultural Systems (2018). DOI: 10.1016/j.agsy.2018.11.005

The New Food Fights: U.S. Public Divides Over Food Science. Differing views on benefits and risks of organic foods, GMOs as Americans report higher priority for healthy eating DECEMBER 1, 2016 https://www.pewresearch.org/science/2016/12/01/the-new-food-fights/

Addressing Gender Inequality in Developing Countries Through Crop Improvement

A lot of production related benefits of biotechnology have been discussed in the literature, for instance decreased greenhouse gas emissions (Brookes and Barfoot, 2017), reduction in exposure to toxic chemicals (Kouser & Qaim, 2011), and food safety(Munkvold et. al, 1999). However additional research indicates that there may also be social benefits related to gender equality as discussed in
Social and Economic Effects of Genetically Engineered Crops (National Academies of Science, 2016).

Below are some highlights from this research:

• Women comprise a significant proportion of agricultural related labor in developing countries (~43%)
• Women in developing countries face significant challenges related to access to education, information, credit, inputs, assets, extension services, and land 

The adoption of biotechnology in developing countries has had some mitigating effects:

• In India biotechnology adoption (Bt cotton) resulted in increased work hours and income for women (Subramanian and Qaim, 2010)
• Reduced exposure and freeing women from spraying toxic chemicals and related labor (Bennett et al., 2003; Zambrano et al., 2013; Zambrano et al., 2012; Smale et al., 2012)
• Increased importance of women in decision making within households (Yorobe and Smale, 2012; Zambrano et al., 2013; Rickson et al., 2006

References:

National Academies of Sciences, Engineering, and Medicine; Division on Earth and Life Studies; Board on Agriculture and Natural Resources; Committee on Genetically Engineered Crops: Past Experience and Future Prospects. Genetically Engineered Crops: Experiences and Prospects. Washington (DC): National Academies Press (US); 2016 May 17. 6, Social and Economic Effects of Genetically Engineered Crops. Available from: https://www.ncbi.nlm.nih.gov/books/NBK424536/

Graham Brookes & Peter Barfoot (2017) Environmental impacts of genetically modified (GM) crop use 1996–2015: Impacts on pesticide use and carbon emissions, GM Crops & Food, 8:2, 117-147, DOI: 10.1080/21645698.2017.1309490

Bennett R, Buthelezi TJ, Ismael Y, Morse S. Bt cotton, pesticides, labour and health: A case study of smallholder farmers in the Makhathini Flats, Republic of South Africa. Outlook on Agriculture. 2003;32:123–128.

Kouser, S., Qaim, M., Impact of Bt cotton on pesticide poisoning in smallholder agriculture: A panel data analysis,Ecol. Econ. (2011), doi:10.1016/j.ecolecon.2011.06.008

Comparison of Fumonisin Concentrations in Kernels of Transgenic Bt Maize Hybrids and Nontransgenic Hybrids. Munkvold, G.P. et al . Plant Disease 83, 130-138 1999.

Rickson ST, Rickson RE, Burch D. Women and sustainable agriculture. In: Bock BB, Shortall S, editors. Rural Gender Relations: Issues and Case Studies. Wallingford, UK: CABI Publishing; 2006. pp. 119–135.

Smale M, Zambrano P, Paz-Ybarnegaray R, Fernández-Montaño W. A case of resistance: Herbicide-tolerant soybeans in Bolivia. AgBioForum. 2012;15:191–205.

Subramanian A, Qaim M. The impact of Bt cotton on poor households in rural India. Journal of Development Studies. 2010;46:295–311

Yorobe JM Jr, Smale M. Impacts of Bt maize on smallholder income in the Phillipines. AgBioForum. 2012;15:152–162

Zambrano P, Smale M, Maldonado JH, Mendoza SL. Unweaving the threads: The experiences of female farmers with biotech cotton in Colombia. AgBioForum. 2012;15:125–137.

Zambrano P, Lobnibe I, Cabanilla DB, Maldonado JH, Falck-Zepeda J. Hiding in the plain sight: Women and GM crop adoption. Paper presented at the 17th ICABR Conference: Innovation and Policy for the Bioeconomy, June 18–21. Ravello, Italy: 2013.

CRISPR and Gene Editing Blueprint Analogy

There was a nice guest post on AgWeb recently titled:

Gene Editing: Building Better Blueprints, One Gene at a Time. 

I really like the blueprint analogy as a means to help people understand how gene editing is similar and different from other technologies. Many consumers that are skeptical of advances in food technology are comfortable with older technologies or don't realize the differences.

As Bob Reiter stated in the article:

"Now consider this: what if there was a defect in the blueprint for the house? If we followed those instructions anyway, the defect would be built into the house – which could later lead to structural problems, ranging from minor to catastrophic, depending on which part the defect involved."

Messing up the blueprint is what a lot of consumers are hesitant about when it comes to traditional recombinant DNA technologies (a.k.a. 'GMOs'). They are worried about unknown downstream structural problems and the impact that could have on human health. To put this in other terms, genomic disruptions. In response they advocate for more regulations, testing, and labeling of 'GMO' foods and many are calling for a similar framework for gene edited foods. And food manufacturers take advantage of the marketing opportunities created by these concerns. Ever heard of the non-GMO Project?

But the blueprint analogy is actually helpful here. As Bob explains, in college he was careful about designing the blueprint. If we think of gene editing like making careful targeted changes to the blueprint, USDA organic approved technologies (methods using radiation or chemical mutagens) are more like his intoxicated fraternity brothers sneaking in making random changes to his plans without him knowing. Perhaps they introduce really cool innovations! On the other hand, the roof might leak, the plumbing could drain backwards, or worse. To put it differently, the number of genomic disruptions are far greater and unknown.

You would think, if customers and regulators were concerned about Bob's targeted changes (maybe they would insist that someone from the county does an inspection before proceeding with the construction) they would really be worried about the changes brought about by his inebriated counterparts. However, if we analogize back to mutagenic conventional and organic food, they don't seem concerned at all. They have accepted a build it and see what happens later attitude. No testing. No labeling. (other than maybe that Butterfly food marketers like to stamp on everything from rock salt to water). Of course, from a scientific risk based perspective, there probably is not a reason for testing or labeling these foods....if consumers already understand and accept this that should be a step further down a path toward newer 'safer' technologies that promise so much more.

You can only take an analogy so far but I like Bob's.

See also: Organic Activists Realize Hypocrisy On Gene Editing and Biotech

References:

Batista R and others (2008). Microarray analyses reveal that plant mutagenesis may induce more transcriptomic changes than transgene insertion. Proceedings of the National Academy of Sciences of the United States of America 105(9): 3640–3645

Baudo MM, Lyons R, Powers S, Pastori GM, Edwards KJ, Holdsworth MJ, Shewry PR. (2006). Transgenesis has less impact on the transcriptome of wheat grain than conventional breeding. Plant Biotechnol J. 2006 Jul;4(4):369-80

The convergence of data science, genomics, and technology

This is a couple years old but just as relevant and inspiring:

"I believe the future of agriculture will be shaped by continued improvements in efficiency. By more effectively applying the advancements that have come from the green and biotech revolutions, we can continue to see yield increases across the world. We can use data science to drive agronomic practice improvements that enhance existing technologies, like advanced seed genetics. Data science technologies can build upon the yield potential already present in each seed by providing farmers with actionable insights they can use to drive efficiency improvements on their operations, getting the most out of every single plant." - Erik Andrejko, Director of Science, Head of Data Science at The Climate Corporation (2015)

From: https://climate.com/blog/applying-data-science-to-enhance-farming-practices

See also: Monoculture vs. the Convergence of Big Data and Genomics

Why Study Plant Breeding and Genetics?

A really nice piece in the Huffington Post by Kevin Folta evangelizing about future careers in plant breeding (link).

I like the way he describes genetic markers and marker assisted selection:

"The trick is to use DNA-based landmarks that physically travel through generations in parallel with the trait of interest. A simple DNA test, much like those used in forensic science, tells a breeder if a seedling has a high likelihood of presenting a coveted trait upon maturation. These tests allow breeders new ways to identify the most relevant parent plants, and eliminate non-candidate offspring at the seedling stage, long before valuable time, labor, acreage, fertilizers, and water are invested."

When I was an undergraduate senior, we had a speaker working on their PhD from UC Davis visit WKU to discuss their work in quantitative genetics. That was the first time I realized that there was lots of opportunity and very exciting work that could be done outside the lab, as genetics and crop improvement can be as much a statistical and quantitative science as a lab science. In other words, not all of the cool jobs were being taken by PhDs in molecular biology. As Kevin states:

"While mastery of molecular biology techniques is not required, modern breeders need to be conversant in the technologies, and excited to integrate with collaborators. A modern breeder will have strong skills in statistics, and even computational prowess."

This sounds a lot like a data scientist with domain expertise in plant breeding and genetics. From this angle there seems to be tremendous opportunities with the convergence of big data and genomics. While I did not pursue a career in plant breeding, I have leveraged my quantitative training in agronomy including experimental design and statistical genetics blended with econometrics and programming to advance my career in healthcare data science.

Think about the combination of genomic markers, plant 'wearable' sensors, remote sensing, and other precision ag data sources. Very exciting work for all involved.

Dr. Folta is an excellent evangelist for crop genetic improvement and science communication.  I highly recommend checking out his podcast Talking Biotech where each week he or Dr. Paul Vincelli from the University of Kentucky discuss agricultural and medical biotechnology.

Related: QTL Analysis and Marker Assisted Selection 

Herbicide Resistance and GE Crops - Thinking like an economist

In a recent issue of Weed Science, Andrew Kniss investigates the relationship between herbicide resistance in weeds and adoption of genetically engineered crops.

There is a popular story with an anti-gmo theme that holds that genetically modified crops tolerant to roundup (glyphosate) herbicide have encouraged excessive levels of use. This has lead to a build up of roundup resistant weeds. In some sense, this paper may provide some evidence in favor of that story. However, I think there is a tendency among critics to extrapolate further that GMOs lead to higher levels of weed resistance (in general). The storyline does not make a distinction between weed resistance in general and specific resistance to roundup.

What this paper does indicate is that there are to some degree externality mitigating aspects of glyphosate tolerant crops. (not exactly but somewhat like the positive externalities we have seen with Bt crops). As stated in the abstract, "Increased glyphosate use in cotton and soybean largely displaced herbicides that are more likely to select for herbicide-resistant weeds, which at least partially mitigated the impact of reduced herbicide diversity....the evolution of new glyphosate-resistant weed species as a function of area sprayed has remained relatively low compared with several other commonly used herbicide SOAs."

This paper definitely provides data and evidence contrary to some of the popular stories condemning biotechnology. Stories that don't look at crop production from a more comprehensive systems based viewpoint miss these nuances. Hmmm....it appears that thinking like an economist (systematically considering intended and unintended consequences) has a lot to lend to the nuances of herbicide resistance.

More from the Abstract:

 "adoption of GE corn varieties did not reduce herbicide diversity, and therefore likely did not increase selection pressure for herbicide-resistant weeds in that crop. Adoption of GE herbicide-resistant varieties substantially reduced herbicide diversity in cotton and soybean. Increased glyphosate use in cotton and soybean largely displaced herbicides that are more likely to select for herbicide-resistant weeds, which at least partially mitigated the impact of reduced herbicide diversity. The overall rate of newly confirmed herbicide-resistant weed species to all herbicide sites of action (SOAs) has slowed in the United States since 2005. Although the number of glyphosate-resistant weeds has increased since 1998, the evolution of new glyphosate-resistant weed species as a function of area sprayed has remained relatively low compared with several other commonly used herbicide SOAs."

Link and Citation:

Kniss, A. (2017). Genetically Engineered Herbicide-Resistant Crops and Herbicide-Resistant Weed Evolution in the United States. Weed Science, 1-14. doi:10.1017/wsc.2017.70

https://www.cambridge.org/core/journals/weed-science/article/genetically-engineered-herbicideresistant-crops-and-herbicideresistant-weed-evolution-in-the-united-states/22B3B07F8EB980D2CFEEE3AA36B7B2C1

See also: 
Game Theoretic Analysis of Bt Resistance
Positive Externalities of Biotech Bt Traits on Non-Biotech Crops and Non Target Insects
Environmental and Health Effects of Bt Cotton
Choices Magazine - Herbicide Resistance

Environmental and Health Effects of Bt Cotton

Kouser, S., Qaim, M., Impact of Bt cotton on pesticide poisoning in smallholder agriculture: A panel data analysis,Ecol. Econ. (2011), doi:10.1016/j.ecolecon.2011.06.008

"While substantial research on the productivity and profit effects of Bt cotton has been carried out recently, the economic evaluation of positive and negative externalities has received much less attention. Here, we focus on farmer health impacts resulting from Bt-related changes in chemical pesticide use. Previous studies have documented that Bt cotton has reduced the problem of pesticide poisoning in developing countries, but they have failed to account for unobserved heterogeneity between technology adopters and non-adopters. We use unique panel survey data from India to estimate unbiased effects and their developments over time. Bt cotton has reduced pesticide applications by 50%, with the largest reductions of 70% occurring in the most toxic types of chemicals. Results of fixed-effects Poisson models confirm that Bt has notably reduced the incidence of acute pesticide poisoning among cotton growers. These effects have become more pronounced with increasing technology adoption rates. Bt cotton now helps to avoid several million cases of pesticide poisoning in India every year, which also entails sizeable health cost savings." 

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Recently I was reading an artcile, "The big Washington food fight" in Politico discussing challenges facing bringing diverse interests and perspectives on food issues under one roof through the Grocery Manufacturers association.

There are a couple things influencing my thinking about this.....the idea that voters and consumers may have systemic biases in their knowledge and preferences in general and specifically about food and technology. The other thing is  related to recent research showing a divergence between public perception of science driven by political leaning....a divergence that widens *with* more education and science knowledge (see http://www.pnas.org/content/114/36/9587 ).

This research was not directly related to food except for genetically engineered food . Biotech related effects were not significant in this paper, but as the article noted the data is from 2006 and perhaps biotech was not nearly as politicized or polarized as it would be reflected in more recent data.

So in this context, what does it mean to say 'the customer is always right' and how do you give the customer what they want?

For instance, sustainable food seems to be high on the list of priorities. However, there are plenty of cases where the most sustainable technology is completely rejected by some segments. I'm thinking here of rBST, various aspects of biotechnology, even processing mechanics like finely textured beef. These are all examples where scientifically, you can produce more food using fewer resources and have a lower carbon footprint.

There seem to be two dominant approaches or paradigms by food companies for dealing with this.

One approach is going all in with the 'negative' or 'free from' labeling regardless of science. This paradigm feigns or fakes transparency in the sense it acknowledges consumer preferences related to knowing 'what is in their food' but adds lots of confusion about substantial differences related to food safety and sustainability. This group is more likely to engage in negative advertising (think Chipotle) and lobbying for regulations related to food labeling requirements (think Vermont). The other paradigm takes a 'less is more' approach in terms of honest disclosure about these technologies.

Production agriculture is caught in the middle. Whichever paradigm becomes the most dominant (both in the marketplace and the ballot box) I fear will determine the fate of the kinds of crops farmers grow and technologies they have access too, types of products we see on the shelves, and the potential for healthier and more environmentally sustainable solutions to challenging worldwide problems.

See also: Food with Integrity is Catching On

Polarization of Controversial Science and Limitations of Science Literacy

I recently reviewed a PNAS article titled "Individuals with greater science literacy and education have more polarized beliefs on controversial science topics". This was interesting along a couple of dimensions.

For one this is similar to research I was working on during my masters degree examining interrelationships between religous, science, and other beliefs and perceptions of genetically engineered foods. However this work had much better data.

The primary finding was that when it comes to certain beliefs about things like climate change, stem cell research, or evolution there are differences influenced largely by politics or religion. This polarization is actually increased as levels of education and science literacy are raised - "Individuals with greater education, science education, and science literacy display more polarized beliefs on these issues."

The authors looked at these effects in relation to genetically modified foods, but results were non-significant. This may be explained by the fact that the data source was ten years old (2006) and perhaps this issue had not become quite as polarized in a political sense like current data may suggest. This begs the question or the hypothesis proposed in the conclusion that "Understanding these mechanisms can guide science communication so that the evidence gets through before it no longer matters" i.e. perhaps before it becomes polarized by political priors.

This is really significant in an age where science literacy is a concern (right that's one reason we just had a march for science). This will ultimately influence the types of products we see on the shelves and the potential for healthier and more environmentally sustainable solutions to challenging worldwide problems.

From an econometric sense,  the polarization was captured in a quantitative sense via interaction terms in logistic regression. Predicted or fitted values were used to plot and visualize the interactions, sidestepping the hassle of attempting to discuss or interpret the estimates from the interaction terms in a statistical sense. Output was provided in the paper and an appendix.

I also would be interested to know more about any related work that may be looking at some of these factors as latent constructs in a structural equation context. 

See also: Voter Irrationality and Systematic Bias

Endogenous Growth Models and Stagnation in Agricultural Innovation

See also: Ideas and Research Productivity in Agriculture 

From: Big Ideas Are Getting Harder to Find - Stanford Business Insights:

"big ideas are getting harder and harder to find, and innovations have become increasingly massive and costly endeavors, according to new research from economists at the Stanford Institute for Economic Policy Research. As a result, tremendous continual increases in research and development will be needed to sustain even today’s low rate of economic growth." 

One of the key equations in the paper relates the number of researchers working in an area to the number of new ideas: research productivity = (A*/A)/S(t) = # new ideas / # researchers One particular area they looked at was with productivity in agriculture, measuring ideas as crop yields.

 “For instance, to measure productivity in agriculture, the study’s co-authors used crop yields of corn, soybeans, wheat and cotton and compared them against research expenditures directed at improving yields, including cross-breeding, bioengineering, crop protection and maintenance....On average, research productivity in agriculture fell by about 4% to 6% per year, the study found....Research productivity is simply the ratio of average yield growth divided by the number of researchers." 

They were careful to tease out research related to non-yield related traits. However, is 'yield' always the best way to measure productivity? Maybe it is a good proxy, but it looks like their data really starts in the 60's....about the time when corn seed was being transitioned away from double cross to single cross hybrids, which were more uniform and higher yielding. In terms of having a large marginal impact on yield, that seems like it could be a tough act to follow, even 40+ years later (its not like we will rediscover hybrid vigor or heterosis) no matter how many PhD's we throw at increasing yield. Other developments in crop improvement may in fact be inflating the denominator in the equation above. The convergence of big data and genomics has opened the door to numerous potential lines of research related to crop improvement, that although may only marginally impact yield provide other significant technological benefits to growers, consumers, and the environment. This would be important, even if yield were constant.

Advances in big data and genomics may have resulted in many more possible needles to search for in a much greater number of haystacks than before. Each crop has a different genome and a seemingly infinite number of biochemical pathways that could be of interest. Then there is the micro-biome and who knows what is next. We just need way more researchers than before.

 I'm not sure how much this applies in other fields, and in some ways this could be making the authors' point....look at all the lines and directions of research and scientists pursuing them vs. actual improvements in crop yields. But again, even if yield were constant, for every bushel of corn/soy produced today, how much have we improved with regard to CO2 reduction, energy use, erosion/leaching, reduction in toxic chemical exposure (thanks largely to roundup and Bt traits), and biodiversity (in terms of non-pest targets)?

Yield just seems to be one outcome among many of importance to creating sustainable development in the agriculture space (although it is important enough to sustainability that we certainly would not want to revert back to older technologies, genetics, and chemistries as some have argued). It would be interesting to see this modeled with traits or variety patents or total hybrids as a measure of new ideas.

References:

Are Ideas Getting Harder to Find? By Nicholas A. Bloom, Charles I. Jones, John Van Reenen, Michael Webb. September 2017Working Paper No. 3592

Modern Corn and Soybean Production. Rober G. Hoeft, Emerson D. Nafziger, Richard R. Johnson, and Samuel R. Aldrich. MCSP. First Edition. 2000.

Environmental impacts of genetically modified (GM) crop use 1996–2015: Impacts on pesticide use and carbon emissions. Graham Brookes & Peter Barfoot. GM Crops & Food Vol. 8 Iss.2, 2017 Genetically Engineered Crops: Has Adoption Reduced Pesticide Use?

Agricultural Outlook ERS/USDA Aug 2000 Greenhouse gas mitigation by agricultural intensification Jennifer A. Burneya,Steven J. Davisc, and David B. Lobella.PNAS June 29, 2010 vol. 107 no. 26 12052-12057

Stawman Arguments Against Statements Related to GMO Safety

Previously I discussed how the World Health Organization, the American Medical Association, and the National Academy of Sciences have all issued statements regarding the safety of foods derived from genetically engineered crops.

In addition I discussed how critics have questioned these statements. One set of assertions supports the invocation of the precautionary principle.

I have written before about issues related to using the precautionary principle with respect to genetically modified vs conventional food crops.

In this post I would like to specifically discuss the assertion that  "There are no epidemiological studies investigating potential effects of GM food consumption on human health."

To those unfamiliar with modern crop science and genetics, that could sound like a very condemning statement. But that begs the question, have there been epidemiological studies investigating the potential effects of conventionally and mutagenically improved crops on human health?

Its also a true statement that there are no epidemiological studies investigating the relative safety of using the stairs vs. elevators vs. escalators vs. leaping out the top floor window with regard to human health. (although I am sure actuaries have assessed property/casualty probabilities associated with similar kinds of risks related to building design, we don't have people losing sleep over lack of publication in this area)

These last examples might seem extreme and unrelated, but they illustrate the point that for some things, conducting an expensive (and difficult) epidemiological study to assess impacts on human health makes little practical sense. 

What reasoning would make us think this is necessary for genetically modified foods? If we were discussing inclusion of traits known to impact metabolism or hormone levels or some other biological function this might make sense. But the types of crops approved for human consumption don't have traits known to behave this way. Some critics might assert that it is the unknown consequences (changes in DNA, changes in proteins, or metabolism) that we should be worried about. 

However, scientists know that these kinds of genetic disruptions are not any more proliferate with genetically engineered crops than those related to traditional and mutagenic crop improvement that have been consumed and accepted by consumers without question for hundreds (thousands) of years or more in some cases and decades in others. They are substantially equivalent in this regard.

It turns out that the statement about the absence of epidemiological studies is really irrelevant when it comes to assessing the risks associated with genetically engineered food consumption. Arguments using epidemiological studies to form a psychological baseline or frame of reference are akin to strawman statements that could raise unnecessary doubts and fears about a technology that actually exhibits characteristics beneficial to human health and the environment.

References:

No scientific consensus on GMO safety. Environmental Sciences Europe. 2015 27:4

Batista R, Saibo N, Lourenço T, Oliveira MM. Microarray analyses reveal that
plant mutagenesis may induce more transcriptomic changes than transgene
insertion. Proc Natl Acad Sci U S A. 2008 Mar 4;105(9):3640-5. doi:
10.1073/pnas.0707881105. PubMed PMID: 18303117; PubMed Central PMCID: PMC2265136

Baudo MM, Lyons R, Powers S, Pastori GM, Edwards KJ, Holdsworth MJ, Shewry PR. (2006). Transgenesis has less impact on the transcriptome of wheat grain than conventional breeding. Plant Biotechnol J. 2006 Jul;4(4):369-80

First citation: