Table of Contents
The Chicken or the Egg: Does Interdisciplinary Collaboration
Enhance Systems Model-Building or Does Systems Model-Building
Enhance Interdisciplinary Collaborations?
Jessica L. Durfee’, Jim I. Mills’, Philip C. Emmi’, and Craig B. Forster’
University of Utah
‘Department of Communication
255 S. Central Campus Drive - LNCO 2400
Salt Lake City, UT 84112 USA
(801) 363-6285
jessicadurfee@yahoo.com
College of Architecture + Urban Planning
*College of Architecture + Urban Planning
“Department of Geology and Geophysics
Abstract: Does interdisciplinary team collaboration enhance the knowledge generated and
shared in systems model building or does model building enhance the collaboration and
collegiality of interdisciplinary team members? This paper addresses the processes involved in
constructing dynamic system models of “deep complexity” related to scientific and
environmental issues. Typically such model-building requires the knowledge and expertise of
specific disciplinary specialists. The examples used in this analysis are descriptive evidence of
one team’s efforts to model an urban airshed, which requires input from atmospheric chemists,
biologists, engineers, meteorologists, and urban planners — just to name a few. The authors
argue that addressing an emergent class of problems characterized by deep complexity requires
an iterative process of interdisciplinary communication facilitated by model building. This
model building, it is argued, serves as the “Rosetta Stone”, which can lead interdisciplinarians
to create an organic narrative to describe the system and enhance the quality of knowledge
generated.
Keywords: interdisciplinary research, deep complexity, collaborative model building, system
dynamics modeling.
INTRODUCTION
What comes first, the chicken or the egg? Does interdisciplinary team collaboration
enhance the knowledge generated and shared in systems model building? Or does model
building enhance the collaboration of interdisciplinary team members? The answer is “yes!”
Interdisciplinary collaborations and mediated modeling activities simultaneously and mutually
improve each other. In the spirit of the conference theme, “collegiality”, this paper explores
interdisciplinary collegiality and how the processes involved in constructing system models of
complex scientific and environmental issues can create a symbiotic relationship between the
scientists and the model-building.
Models of “deep complexity”, which address issues such as biocomplexity or
environmental science require the knowledge and expertise of many various disciplinary
specialists. Specifically, the examples presented in this paper illustrate how modeling an urban
airshed (i.e., the air quality in cities and heavily developed and populated areas) requires input
Chicken or Egg? 2
from atmospheric chemists, biologists, engineers, meteorologists, and urban planners — just to
name a few. This paper is based upon a descriptive study of a successful interdisciplinary team in
order to illustrate the dynamics of human-model interactions. Before introducing the case study,
it is important to first explain the significance of interdisciplinary research teams and systems
modeling when tackling issues of “deep complexity”.
Following a condensed review of the literature on interdisciplinary team research and
systems modeling of complex science, the case study of an interdisciplinary team in Salt Lake
City, Utah, USA will be introduced. The case study will first provide an overview of the
interdisciplinary team and their goals. Second, examples of how interdisciplinary collaboration
has enhanced the quality and accuracy of the systems model produced will be shared. Finally the
case study includes examples of how the modeling process enhanced interdisciplinary
communication and collaboration in this team.
The research question posed (Does interdisciplinary collaboration enhance model-
building or does model-building enhance interdisciplinary collaboration?) leads to one answer,
“yes!” both processes improve the other. However, it is impossible to differentiate which
process influences and improves the other to a greater degree. Alas, one can not use an “either-
or” mentality to explain the symbiotic relationship of interdisciplinary collaboration and systems
model building. Systems model building improves interdisciplinary communication, because
scientists of all shapes and sizes are able to contribute to a dynamic process. Building a systems
model of complex environmental problems requires the insight of multiple perspectives,
experiences, and disciplines. Likewise interdisciplinary collaboration improves the systems
model produced. And if done correctly the process can lead to creative solutions for complex
problems facing today’s world.
What are Interdisciplinary Research Teams and Why are they Necessary?
Interdisciplinary research is becoming more necessary due to the societal and scientific
complexities of problems, which are insoluble by single disciplines or experts (e.g. pollution,
global warming, issues of land and water use). Complicated scientific issues, especially those
related to the environment, require policy decisions and almost universally involve matters of the
social, physical, and natural sciences. Such decisions need to be informed by interdisciplinary
studies in order to be complete and accurate. Interdisciplinary studies are generally intersections
of various theories, methodologies, and data from more than a single discipline. As complicated
as it may sound, this conglomeration of epistemologies and methodologies is the only way to
sincerely tackle complex scientific and environmental problems (Daily & Ehrlich, 1999;
Heemskerk, Wilson, & Pavao-Zuckerman, 2003; Kostoff, 2002; Naiman, 1999).
It is important to first clarify and define concepts introduced in this section of the paper:
deep complexity; interdisciplinary research and communication across specialization. “Deep
complexity” refers to the physical and natural sciences, sometimes dubbed “hard” science (i.e.
studies in biology, geology, physics, chemistry, and any combination of). Problems of deep
complexity are defined as those issues that are embedded in social, technical and/or policy
relevance, with less emphasis on discipline-related outcomes; e.g. global environmental change,
natural resource management, economic globalization, etc. Importantly, these problems
generally embrace extended temporal and spatial boundaries, and indeed are increasingly of
global significance in space and generational significance in time. This emergent class of
problems is also characterized by “fuzzy” problem boundaries, and beg interdisciplinarians to
arrive at a constructed understanding of what should be included and what should be left out,
Chicken or Egg? 3
which is, of course, a problem system dynamicists face with each new model building exercise.
The terms multidisciplinary and interdisciplinary are often confused or used
interchangeably. In this portion of the paper we will address the differences between
multidisciplinary and interdisciplinary research teams, and in the following section we will
explore how communication across specialization fits into the dynamic of interdisciplinary
research teams.
Research that includes multiple disciplines but each maintains their distinctiveness is
multidisciplinary (Collins, 2002), while research that integrates the multiple disciplines to
effectively form a new unified body of work is interdisciplinary (Kostoff, 2002).
Multidisciplinary research doesn’t include joint planning, management, and review of the
multiple disciplines, as an interdisciplinary research initiative does. In problems of deep
complexity, addressing only one or a few of the component disciplines will result in fragmented
or perhaps misleading results because of neglect of discipline interdependencies — thus the
importance for engaging in systems thinking and model-building. Even if all of the multiple
component disciplines are addressed separately in a multidisciplinary approach, the method of
integrating the multiple facets can affect the final solution. Moreover, the final multidisciplinary
research product will not have the same quality as a unified research product that results from an
integrated interdisciplinary study (Klein, 1990; Klein 1996). Hattery (1986) introduced a
definition of interdisciplinary research which continues to be cited and used, thus this is the
conceptualization that we will use in this paper: Interdisciplinary research is “an integrative
research process, which takes place among researchers with different disciplinary backgrounds”
(p. 13).
Part of understanding how interdisciplinary teams communicate and interact involves
understanding how they negotiate understandings of content relative to different disciplinary
languages and negotiate roles and expectations within the team (Biocca & Biocca, 2002;
Harrington, 2002). Across the globe, scientists are suddenly being asked to transcend their
disciplinary niches and work with other high-level experts in a new type of working group (with
mutually constructed social identity) devoted to solving one problem (Biocca & Biocca, 2002;
Rogers, 2002).
Among the major epistemological models that are being discussed as appropriate for
engaging in this emerging cross-disciplinary research, two stand out and are referred to as Mode
1 and Mode 2 interdisciplinary research (The UK Economic and Social Research Council ,
2004), Mode | interdisciplinary research “aims to further the expertise and competence of
academic disciplines themselves, e.g. through developments in methodology which enable new
issues to be addressed or new disciplines or sub-disciplines to be formed” (4 2). Mode 2 research
is defined as research that addresses “issues of social, technical and/or policy relevance with less
emphasis on discipline-related academic outcomes” (3). It is this second mode of
interdisciplinary research that is being promoted as the solution approach for an emerging class
of problems characterized by deep complexity.
A co-principal investigator in the interdisciplinary research project of our case study
(whom will be formally introduced later) explained his initial experience in interdisciplinarity,
similar to the Mode 1 and Mode 2 conceptions:
For me, the first stage of interdisciplinary work can be called “mutually defensive.” It is
characterized by a reliance on jargon and equations, discussions grounded in
measurements and indexes, puzzlement about the possibility of progress, and mutual
Chicken or Egg? 4
incomprehension. But in the second stage, which I call ‘back to basics,’ we learn to
speak in concepts rather than measurements, we discuss what motivates certain concepts
over others, and begin to draw simple analogies to well known physical realities — yes,
you could call them metaphors. Finally, the end of this stage is characterized by
storytelling. Once we begin to create a mutual narrative we are really engaging in
interdisciplinary communication (personal communication, March 29, 2004).
Beginning with this insight, it is no great leap to imagine an active problem solving
strategy wherein interdisciplinary communication establishes a range of critical problem
elements and space/time boundaries—complete with associated “reference behaviors”—and
model building sharpens the vision. As the UK Economic and Social Research Council
succinctly puts it:
Interdisciplinary research does not occur automatically by bringing together several
disciplines in a research project. Extra effort is needed to promote the formation of a
cohesive research team involving researchers from different disciplines, to combine,
expertise from several knowledge domains and to overcome communication problems
among researchers from different disciplines. Perceived problems in conducting
interdisciplinary research include language and communication issues, institutional
structures and procedures, and divergences in worldviews across disciplines ({ 5).
The emphasis above on “worldviews”, which system dynamicists may recognize as
synonymous with the term “mental models,” is significant, and leads to an understanding of how
system modeling can serve as the foundation stone upon which interdisciplinary mental models
can be coalesced, problem boundaries can be sharpened, and disciplinary communications gaps
bridged.
Communication and Interdisciplinary Research Teams
Understanding of various communication theories and research can facilitate the
complicated collaborations of interdisciplinary teams, as well as provide insight on how to
overcome specialization and truly engage in integrated research efforts. Likewise studies on
interdisciplinarity and the communicative processes of interdisciplinary research can inform the
theories and concepts developed in traditional communication scholarship. Communication
scholars have expertise to offer to the conundrum facing the hard sciences, and it would be
iniquitous to not contribute to the understanding of interdisciplinary team processes. Likewise
understanding how interdisciplinary teams integrate can contribute to building communication
theory and ultimately make applications to contemporary global experiences in interdisciplinary
research. This paper explores one specific area that can enhance the current understanding of
interdisciplinary communication — the use of model building as a collaborative team process, but
first how do members in an interdisciplinary team communicate?
One of the co-principal investigators in this project recognized the unique nature of
interdisciplinary collaborations in academia and the communication processes that challenge
collegiality:
This group of people [acts] differently than a corporate group, a research laboratory
group, a government agency group, etceteras. Academics are programmed to be experts
Chicken or Egg? 5
and expound on their disciplinary knowledge. In addition they are trained to look for
holes in others’ arguments — at times to the detriment of team building (personal
communication, March 22, 2004).
Another major obstacle in interdisciplinary collaborative research is that it is difficult to
find a common language because of discipline specialization (Bauer, 1990; Benda, et al. 2002;
Glantz & Orlovsky, 1986; Sarewitz, Pielke, & Byerly, 2000; Wear, 1999). Glantz and Orlovsky
(1986) recognize the example of the word “desertification” which has various different
definitions in scientific disciplines such as climatology, soil science, meteorology, hydrology,
geography, political science, economics, and anthropology. These differences in definition lead
to miscommunication among researchers and between researchers and policy makers. Any
disciplinary effort requires analyzing language, specifically definitions, terminology, and
metaphors used in order to improve understanding and construct an integrated framework.
Finding a “common language” (or at least agreeing on how to communicate) is often a
dialectical process (Broido, 1979; Davis, 1978; Klein, 1990). Dialectics are discourse between
two or more speakers who express two or more positions or opinions. Davis (1978) suggested
that dialectic is the interdisciplinary method, since interdisciplinarity is achieved when
disciplinary differences are stated, clarified, and then resolved in order to produce a synthesis.
Thus, dialectics can manifest in conversation, dialogue, personal e-mails and public discussions.
Communication theorist, Leslie Baxter, defined a dialectical perspective of interpersonal
relationships, and this can be applied to the relationships that develop in interdisciplinary
research groups. Similar to Davis and Broido’s definitions, Baxter added that “a dialectic is a
tension between two or more contradictory elements of a system” (1993, p. 140). Baxter (1988,
1992, 1993) has used dialectical analyses to examine the way a relational system develops and
changes, how it moves in response to these tensions; and how strategic actions taken by
participants in the system attempt to manage the conflict or contradictions that arise. Likewise a
dialectical analysis may improve an interdisciplinary team’s understanding of differences and
tensions. Without a formal, meta-analysis of team communication, systems modeling efforts can
simulate the processes of a dialectical analysis by forcing team members to publicly articulate
their different opinions and perspectives.
Broido (1979) was one of the first scholars to demonstrate how the dialectical approach
can be a practical methodology for overcoming disciplinary entrenchment in problem-oriented
work. In his essay, Broido (1979) argued that, through a dialectical process members of an
interdisciplinary team begin to see the price of reductionism and the interdisciplinary strength of
integrating given disciplinary frameworks. Likewise possibilities for exporting and importing
disciplinary methods and terminology become more apparent. Dialectics allow for
misunderstandings, animosities, and competitions to be taken seriously and not glossed over
(Broido, 1979; Klein, 1990; Klein 1996) — forcing a higher understanding through intense
communication.
Thus, I’d argue that the dialectical method is imbedded in dynamic systems model
building activities. Interdisciplinary team members engage in a dialectical process when
deciphering the various model components that scientists may insist on including, or conversely
may avoid including. Taking a dialectical perspective to interdisciplinary communication
facilitates a reflexive understanding for exploring language and how it gets used in debates and
discussion of complex science.
Chicken or Egg? 6
In summary, interdisciplinary research teams are typically small groups of “hard”
scientists engaged in an integrative research process in order to address complex, sometimes
global, scientific problems. Members of such teams struggle to overcome disciplinary
differences, which manifest in their different uses of language and metaphor. Successful
interdisciplinary teams engage in some type of dialectical process to better understand each other
and the complex information exchanged, and that dialectical process may influence the building
of a systems model — at least impact the collegiality generated in the process.
CASE STUDY: AN URBAN AIRSHED RESEARCH TEAM
The UTES Project: An Urban Airshed Research Team
For the past year I have been working with The Salt Lake Valley Airshed — Urban Trace-
gas Emissions Study (henceforth the UTES project). The UTES project is funded by the National
Science Foundation to study the complex factors affecting emissions of carbon dioxide, water
vapor, and volatile organic compounds in the valley surrounding Salt Lake City, Utah. This
research requires the interdisciplinary expertise of atmospheric scientists, social scientists, urban
planners, and ecologists. Such collaborations include measuring the concentrations and
emissions of the pollutant gases, tracing their origins, and evaluating the implications for
effective management of the urban airshed. Project members are also engaged in developing a
systems dynamics model that can be used to better understand the complexity of the urban
airshed system as a whole. Eventually the team plans to introduce this model to members of the
local public in an outreach initiative based on public participation and local decision-making.
Hopefully the model will assist the participants in evaluating urban growth and air quality policy
options.
The UTES team is lead by five principal investigators (PIs); with over 50 participating
members from the University of Utah and the Salt Lake community of scientists,
environmentalists, and bureaucrats. Funded participants are primarily from the University, and
the “community partners” include representatives from local government, non-profit, and
environmental agencies. The University partners are a mix of regular faculty and non-teaching
research faculty, likewise there is a range of age and academic seniority — one person is still
working towards tenure, and one person will retire in two months. There are four working
groups that currently communicate via overlapping membership with each other. The four
working groups are (1) Emissions Inventory, (2) Process Studies, (3) Emissions Management,
and (4) Education and Outreach.
Every other week all of the members convene at a lunch seminar in which one member of
the UTES research team or an outside disciplinary expert makes a formal presentation. I have
attended all of the meetings and through participant observation and ethnographic fieldwork I
have gleaned some insight and observations on how scientists from the most specialized fields
are able to interact and progress in a complex research initiative. Communication researchers
who take an ethnographic perspective focus on the speech community as the primary unit of
analysis. A speech community is a “universe of discourse with a finely organized, distinctive
pattern of meaning and action” (Philipsen, 1992, p. 4) Gerry Philipsen (1992) described three
underlying assumptions of this type of data gathering: Speaking is structured (i.e., social rules
guide the code of communication within a group). 2. Speaking is distinctive (i.e., speech
communities differ among cultures and circumstances). 3. Speaking is social (i.e.,
communication is not just a medium for accomplishing a task, it is a part of social life and
necessary for the construction of social identities). Thus, data collected under this paradigm
Chicken or Egg? 7
encourages the development of a case study. A case study is an investigation of a “specific,
unique, bounded system” (Stake, 1994, p. 237). Using Stake’s (1994) definition, the following is
a group of “collective-case studies” in which a number of cases are studied for the insights they
provide to the broader category of similar cases (p. 239). Therefore snapshots of the team’s
experiences in interdisciplinary collaboration and model building efforts will be illustrated in the
following sections.
The Chicken: Interdisciplinary Influence on Model Building
As the literature and history of science has indicated, “two heads are better than one.”
Issues of complex science are best informed by a variety of disciplinary experts. In the case of
the UTES project, modeling initiatives have involved many members of the team, but the most
significant collaborations are not among the modelers themselves, but between the modelers and
the scientists that are gathering measurements or creating an inventory of emissions and air
quality data. Via these interactions the “measurers” gain an appreciation for the “modelers” and
the “modelers” gain a deeper understanding of the data the measurement experts are collecting
and synthesizing. Ultimately this dialect enhances the comprehensiveness and accuracy of the
model. We will cite two examples from the UTES project that illustrate how interdisciplinary
collaboration enhanced the capacity of systems models created.
Example 1: The urban heat flux debacle.
The theory surrounding urban heat fluxes is a premier example of an issue of “deep
complexity” — but it is necessary to create models of air pollution dispersion, urban mixing depth
and mesoscale airflow (Grimmond & Oke, 2002). After an “annual project review” meeting,
meteorologist brought this to the attention to members of the Emissions Inventory working
group. The discussion grew among those collecting measurements regarding the possible
influence of urban heat fluxes. The issue became more complex when local geographic features
were added to the discussions (the Salt Lake valley is nestled between two large mountain
ranges), ultimately the engineers and atmospheric scientists, through a dialectical process,
developed a deeper understanding of how urban heat fluxes might operate in the local system.
The next challenge was for the scientists in the Emissions Inventory working group to explain
how urban heat fluxes would fit in to the air quality model. The modelers met with the
measurement scientists to discuss how to proceed with model-building, and after three long
meetings — they were still stuck in “Mode 1.” Finally, an undergraduate research assistant in the
mechanical engineering department found an article in the Journal of Applied Meteorology and
shared it with the hybrid group. The article explains urban heat fluxes and provides a model for
the engineers to calibrate as well as a background story for the modelers to depict. Ultimately
that article brought disciplinary experts to a common understanding of the urban heat island
phenomena and facilitated the development of a more inclusive, possibly accurate systems
model.
Example 2: The ““TreeSim” component.
This is another example of how disciplinary expertise increased the accuracy and
plausibility of model scenarios. The modeler met with the lead principal investigator for the
project, a biologist, in order to construct a model of the local urban forest, and determine where
this component fit into the larger air quality model. The two worked together to create a working
model. After reaching a shared understanding they invited one of the community partners on the
project — an urban forester. The urban forester provided substantial additional data and local
Chicken or Egg? 8
reference behavior information. This extended insight enhanced the strength of the model and
ultimately increased our understanding and knowledge of the local forest system
The Egg: Model Building Influence on Interdisciplinarity
Collaboration is likely to fail when scientists communicate poorly, have unrealistic
expectations of one another, and internalize prejudices about alien academic fields (Heemskerk
et al., 2003; Turner & Carpenter, 1999). Research has indicated that the development of
conceptual models can guide and facilitate interdisciplinary communication (e.g, Heemskerk et
al., 2003). As discussed earlier, communication across disciplines is challenging, but engaging
in a model-building process, specifically a systems dynamics model, can enhance the dialectal
process in interdisciplinary research.
Constructing a model of the urban airshed requires that disciplinary experts engage in
dialectics that might have been avoided otherwise. Again, these dialectics have not only
improved the system dynamics model, but they have also substantially improved the team
dynamics. We will now share three brief examples of the positive influence systems model
building has had on collegiality in the UTES project.
Example 1: The team first meets modeling.
After three months of informational presentations by each disciplinary expert, the team
was barely beginning to see where their research and specialties intersected. Finally the P.I.s
organized a meeting for all of the partners. It was at this meeting that one of the co-P.I.s first
presented the idea of systems dynamics and collaborative model building. While everyone knew
that the goal of the project was to ultimately create a systems model of the local airshed, many
members didn’t understand how that process was going to evolve, in fact, nearly everyone was
spending their energy and resources determining the breadth and depth of technical atmospheric
measurements. But at this meeting, a few members began to recognize the importance of
integrating data and knowledge — and this lead to increased conversations and information
exchanges. Even the lead PI, who was skeptical of modeling capabilities from the onset was
now visualizing extensive interconnections and articulating them with other team members.
These conversations did not stop at 5:00 PM when the meeting adjourned — they continued for
months, and still continue today, as these researchers use the model as a place to exchange
expertise. “Building the model” is not necessarily the focus of the project, but it keeps the
project focused.
Example 2: The spaghetti bowl.
After six months of discussion among the team members, one of the modelers created an
introductory model map to explain the initial interconnections that they team had identified.
This diagram was presented to all of the team partners and external review board. When first
introduced, you heard members gasp, “Oh my!” Look at that ‘spaghetti bowl!” After a few
quiet moments, members around the room began to interject places where they say their
discipline could contribute — specifically the meteorologists and GIS (geographic information
system) specialist. While these two experts were immediately verbose, many others made
follow-up comments in the next meeting. Thus, the presentation of the model generated a
conversation in which the focus was recognizing the interconnections of each other’s work.
Example 3: Joe’s eureka moment.
While we’ve avoided using names throughout this analysis, this particular example has
come to be known to the modeling team as “Joe’s eureka moment” thus it is impossible to
continue protecting Joe’s anonymity. Joe is a mechanical engineer, and a co-PI on the UTES
Chicken or Egg? 9
project, he was also involved in the “urban heat flux debacle.” In fact his eureka moment was
born in the fourth meeting of the modelers and measurers, after the article from the “Journal of
Applied Meteorology” was shared. One of the other co-PIs shared his interpretation:
You should have seen Joe! He nearly jumped out of his seat! We started with that
LUMPS paper [the article from the Journal of Applied Meteorology], it has a nice,
simple model dealing with vegetation, heat fluxes, and urban development. We started
with pictures from two points of view, Phil and I started seeing aSTELLA model, and
Joe’s crew was envisioning the LUMPS model. Then we discussed the differences
between the two models and that is when Joe had his eureka moment. He was finally
primed to understand what a STELLA model could explain, and how his model would
inform the STELLA model. Joe actually suggested putting together a simple model! I
felt like he finally had an appreciation for what we do (personal communication, March
18, 2004).
This interaction among isolated disciplinary experts would have never happened if it
weren’t necessary to discuss the deep complexity of the urban heat flux phenomenon and how it
impacts air quality. Thus the model provided a forum for the researchers to find an appreciation
for each other, and ultimately a deeper sense of respect and collegiality. Which in turn, improves
the interdisciplinary collaboration and quality of knowledge generated from the team’s efforts to
model the dynamic system of the local airshed.
DISCUSSION
The examples from the UTES project are merely snapshots of experiences in an
interdisciplinary team commissioned to develop a systems model for an issue of deep
complexity. Around the world scientists are being asked to collaborate and integrate their
expertise in order to better understand the global magnitude of complex, scientific issues. These
collaborations are not piecemeal efforts to enhance our understanding of the world, but
complicated, deliberated dialectal interactions. Experts trained in one mode of thinking — one
paradigm of thought — one worldview are now being challenged to see other mental models that
they didn’t even know existed. Both challenges can be overwhelming, first to tackle issues of
deep complexity, and second to work with other “alien” scientists at the boundary of one’s own
discipline. However, the process and the outcome may be enhanced by engaging in systems
model building.
Again, building the model is not necessarily the focus of the project, but it keeps the
project focused. Likewise improving communication across disciplines is not the goal of the
project, but it can become a valuable commodity for improving our understanding of the very
complex world we inhabit. In conclusion, it is not a question of which comes first, the chicken
or the egg, it is recognition that both exist simultaneously and mutually influence each other to
create a dynamic foundation for collegiality and enhanced problem solving.
Acknowledgments. This research is funded by the National Science Foundation Biocomplexity in
the Environment Program, award number ATM 02157658. The authors would like to thank the
anonymous reviewer for his/her insight as well as Dr. Marouf Hasian for his feedback. We'd
would also like to recognize the members of the UTES team, especially the co-principal
investigators: Diane Pataki, Joe Klewicki, Tarla Rai Peterson, Craig Forster, and Philip Emmi.
Chicken or Egg? 10
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