A key context for quantitative modeling is the epistemological background of a method: what is the underlying reasoning for how we believe we can learn about the world from the method? In my experience, understanding this is surprisingly difficult when canned software (whether open-source or proprietary) makes it easy to apply methods without being familiar with the underlying assumptions. Fortunately, I have found abundant online resources and training courses regarding the underlying assumptions of models and have been able to ask computer science and statistics colleagues to explain the often impenetrable documentation and underlying assumptions accompanying canned procedures. In more deeply investigating the epistemological background of statistical methods I have used in my work, I found that the history of the methods is important. For example, Fisher (1926), in the famous statistics paper that originated the criterion of p<0.05, originally mentioned that criterion as a guideline in illustrating a point -- Fisher even used the phrase "personally, the writer prefers." Perhaps in a particular case, p < 0.01 is more appropriate, or p < 0.10. Consulting the history of science literature, I found Porter (1996) summarizing the philosophy of Pearson (of the ubiquitous Pearson correlation in statistics): "A correlation, after all, is not a deep truth about the world, but a convenient way of summarizing experience." So Pearson's correlation was always intended to be used conservatively when drawing conclusions. More recently, statisticians Gelman and Loken (2014) suggest that we should be conservative in our claims regarding p-values, while Spiegelhalter (2017) suggests emphasizing effect sizes (rather than p-values) and using p-values informally in exploratory analyses. Several statisticians have argued that "Hypothesizing After Results are Known" (HARKing) is less problematic when authors are explicit about the exploratory nature of their work (Gelman and Loken 2014, Munafò et al. 2017, Rubin 2017a, Rubin 2017b). These are examples from statistics, but epistemological guidelines and histories exist for all types of modeling, and modelers could seek them out for their particular context.

A final note on epistemic guidelines: once I felt confident in the epistemic underpinnings of a method, if the restrictions of a particular quantitative epistemology felt uncomfortable, I have investigated alternatives. Hypothesis testing uses a construction of "null" versus "alternative," and either of these terms could be interpreted negatively or dismissively, while model selection approaches (Burnham and Anderson 2002) do not label or conceptually privilege one model over another, instead ranking a set of models based on predictive fit and parsimony. This conceptual framing may be desirable if some of the models reflects the knowledge of a marginalized group (e.g. Indigenous people, women, people of color, etc) because small differences in framing can have subtle but important impacts on research participants and collaborators. Also, Bayesian inferential philosophy in the form of "updating" prior knowledge with data appealed to me because it could more closely mirror aspects of learning (e.g. constructivist educational psychology, in which learners build on what they already know, McInerney 2013) than hypothesis-testing strategies.

Another way of conceptualizing "giving context to modeling" is to provide more detail on the experimental apparatus, broadly construed, as a "data biography." Feminist studies scholar Barad (2007) points out that researchers of all kinds really study phenomena rather than objects. She quotes physicist Niels Bohr’s conclusions regarding the wave-particle duality of quantum physics: "the unambiguous account of proper quantum phenomena must, in principle, include a description of all relevant features of the experimental arrangement." Barad extends this to the political and social contexts of an experiment along with the description of experimental equipment. Along the same lines as this broader "apparatus list," science and technology studies scholar Taylor (2010) suggests mapping the connections of one's work: "things that motivated, facilitated, or constrained [your] inquiry and action." In addition, sociologist Clarke (2005) suggests that the experimenter themselves is a part of the apparatus to be described, and Indigenous scholars Walter and Andersen (2016) point out the need for the researcher to clarify their research standpoint. (See Suppl. material 1 for my description of how my background and values have influenced the process and content of this paper. Note that self-disclosure as I have chosen to do it for this paper may or may not be appropriate for other researchers or situations.)

Acknowledging that modeling frequently does not proceed in a linear, logical fashion -- and that recording the reasons for various decision points may aid understanding of how the final result came about -- is recognized by both engineers, suggesting describing modeling "paths," (Lahtinen et al. 2017), and by geographers and environmental scientists who seek to reveal the modeling "improvisation" process (Landström et al. 2013). Some methods have been proposed to give more background detail on models, for example TRACE (TRAnsparent and Comprehensive Ecological modelling documentation, from complexity modelers Grimm et al. 2014). Writing a data biography is also a way of "re-centring the agency of human actors [scientists] in generating, collecting, and collating big data," (from geographers and environmental scientists Salmond et al. 2017) or reporting on the "relational empiricism" of your work: being attentive to "the relations that constitute its objects of study, including the investigator’s own practices" (from science and technology studies and feminist studies scholars Verran 2001, Kenney 2015). Being aware of and representing one's own biases and values will help with triangulation processes so that other knowledge on the topic with different biases can be compared and contrasted, as argued by epidemologists Munafò and Smith (2018).

Methods for writing such "data biographies" could include extending a methods section to describe more details of the work or creating appendices with additional detail. Some journals now offer or suggest venues to publish more detailed accounts of methods (for example, www.protocols.io). One could publish a companion paper which describes the contexts in which the model arose, how the people involved interacted, what each of their backgrounds and perspectives were. In order to write a data biography, one may need to keep a modeler’s notebook or journal to keep track of choices and the reasons for them (proposed by modelers Grimm et al. 2014, Lahtinen et al. 2017). The presence of thorough data biographies would help immensely in the analyses of high-variety big data and reproducibility of existing studies (helping to meet the requirement of "pre-reproducibility," according to statistician Stark 2018), and could also be valuable for historians and science and technology studies scholars. Perhaps interdisciplinary collaborations could provide guidance for modelers about which details could be most useful to record -- details they might not themselves think to mention.

Several issues arise from this idea of a data biography. First, how does one know which details of a context to include as part of the "apparatus list" (raised by geographer Bergmann 2016) -- where does one stop? One proposal is to conduct a thought experiment: would the results change if one substituted something different? If so, it could be mentioned, at least briefly, in the data biography. A similar practice could be employed for describing one's own biases and background, highlighting those aspects that most influenced the choices made in the work. A second issue is that in all forms of scientific transparency, the issue of vulnerable populations and data privacy cannot be ignored. In those cases, it is appropriate to work directly with the populations themselves to determine how to balance their privacy and sovereignty with scientific transparency.

As a final note, I have sometimes been on the other side, trying to re-purpose data or conduct a meta-analysis when the original study does not provide enough detail to model the observation process. In these cases, I have found myself using qualitative research methods (often learning them inefficiently "on the job"), e.g. interviewing the researchers about their methods. I would have benefited from more formal training in ethnographic methods, either via interdisciplinary instruction on mixed methods, getting advice from colleagues with these skills, or directly collaborating with them, in order to more effectively obtain the needed information in an appropriate timeframe.

There can be an element of qualitative data synthesis at work behind quantitative models, which can also be valuable context to acknowledge. Many modelers openly recognize that data can be quantitative or qualitative (Grimm et al. 2014, Elsawah et al. 2017, Munafò and Smith 2018) and there are many modeling methods which can incorporate a wide range of data (examples: agent-based modeling from anthropologist Agar 2003; expert solicitation of Bayesian priors from statisticians and ecologists Kuhnert et al. 2010). Would it be a stretch to acknowledge qualitative reasoning as well? For example, one could qualitatively integrate one's prior (possibly quantitative) knowledge and past experience into the qualitative aspects of models one creates (functional forms, variable choices). This idea is consistent with the "information processing" model of constructivist educational psychology, in which learners build on past experience as they take in new information (McInerney 2013). Acknowledging qualitative thinking and methods need not weaken the credibility of a model: according to educational researcher Le Roux (2017) "rigour does not lie in the chosen method per se, but in the judicious application of the method and explaining how the process was implemented." Clear accounting of qualitative reasoning and applying established qualitative analysis like the "constant comparative method" in Grounded Theory (described by sociologist Charmaz 2014) could improve the rigor of a study through greater transparency in and understanding of the process. And qualitative analysis could also be useful in assessing models: one could use individual cases as a kind of "ground-truthing" of heavily quantified modeling results, treating the model as a narrative (as described by geographers and environmental scientists Millington et al. 2012). It is sometimes challenging, however, to retain and report the qualitative knowledge in a highly quantitative and possibly automated modeling process, particularly in typical Methods sections of journal articles, so modelers could find ways in appendices and other venues to include this information in their data biographies.

As I have reflected on how to define quantitative and qualitative analysis, I found that I iterate between both ways of thinking. As I searched for guidance on what distinguished quantitative from qualitative methodology and how to combine quantitative and qualitative data, I eventually concluded that the two are surprisingly commingled, even in existing modeling practice. I encountered many different definitions of "qualitative," and even having learned qualitative research methods, I see easy ways to flexibly apply quantitative assessments to qualitatively-generated data. Similarly, I can identify many points in a quantitative analysis which involve qualitative assessments. For example: looking at a graph of quantitative measurements and then proceeding based on a qualitative observation about the shape of the graph or the clustering of the points; integrating quantitative knowledge about many different sources upfront into a qualitative sense of what to expect from a model result or model performance indicator; or using qualitative arguments to explain quantitative results. And qualitative approaches to data collection can provide valuable insight into designing model structure or determining model parameters. In many ways, the distinction between qualitative and quantitative methods is less about theoretical differences and more about analytical cultural differences. I am an example of a quantitatively trained researcher who has since learned qualitative methods, and I find that the ability to choose between methods in either category -- and sometimes combine them -- lends richness and rigor to my analysis. And there may be cases where qualitative analysis is taken more seriously if the researcher can speak the language of quantitative analysis as well (e.g. anthropologist Levin 2019).

All models are partial representations, so one way to get a more complete picture is to use "triangulation," a method used by social scientists to bring multiple different datasets or sources of knowledge to bear on the same question (see educational researchers Merriam and Tisdell 2015). Borrowing metaphorically from the mathematical method of locating a point based on distances from other points, this kind of comparative data analysis allows researchers to see where different data connect, substantiate each other, or offer different perspectives. Feminist studies scholar Haraway (1988) suggests that "the knowing self is partial in all its guises, never finished, whole, simply there and original; it is always constructed and stitched together imperfectly, and therefore able to join with another, to see together without claiming to be another." When bringing together multiple partial knowledges, one possible set of outcomes centers on how and where the knowledges intersect or agree (see engineers and epidemiologists Lahtinen et al. 2017, Munafò and Smith 2018). Historical ecology (in which multiple kinds of historical accounts are used to reconstruct how landscapes appeared in earlier times) is a good example of triangulation of different datasets to find agreement (e.g. Grossinger 2012).

Bringing multiple knowledges together does not always result in consensus, however, and allowing for knowledges not to eliminate each other when they do not agree is critical: collaborative modeling should not erase difference (see geographers and interdisciplinary scholars Klenk and Meehan 2015). Where data or analyses do not agree, we can strive to represent the multiple stories that emerge from the models, possibly in the data biography. When accounts conflict, we can benefit from knowing why. Is it because we have not fully understood each account? Is there missing information from one or both? Has one side been marginalized and one enabled and there are political and social reasons why these accounts do not intersect? Is the mismatch due to some difference in the epistemic underpinnings of the two accounts? By investigating these differences we may come to understand more about the system as well as the people studying it, and the methods they (or we) use.

There are established methods for triangulation in the form of meta-analysis. One prominent example from health research is the Cochrane method for combining information from different studies (Higgins et al. 2019). In addition, models can incorporate observation process explicitly, for example statistical ecologists Link and Sauer (2007), who represent the improvement of birders in identifying birds by including an "observer effect" in which each birder's count can increase over time independent of the actual number of birds present. Models like these position the observer as a force in the model rather than ignoring the human process of observation. This could be a way of situating the knowledge of multiple studies relative to each other, and while it may begin as a mainly methodological choice (e.g. representing the uncertainty of the measurement process), explicitly modeling observation processes could provide an entry point for other aspects of situating knowledge (e.g. asking questions about why things were measured, who was involved, and so on). Some possible ways to bring together different datasets and models to see how they agree or disagree include: comparing modeling results or initial data exploration with other researchers' data or with other groups' knowledge (for example, Indigenous or local peoples' knowledge), or with other datasets; formal sensitivity analysis of model parameters and assumptions or subsets of data to determine what model assumptions change the results; using more informal "analytical flexibility" (as per statisticians, epidemiologists, and psychologists Munafò et al. 2017) to check the robustness of results by trying a variety of inputs and choices and comparing the outputs; and/or using the literature review component of the project's write-up as a way to triangulate, reorienting a "background" or "discussion" section of a paper to emphasize the comparison of the results with what other studies have found using different data or methods. These suggestions are not novel modeling or writing practices -- rather, I am suggesting that we frame them for ourselves more explicitly as triangulation processes.

Triangulation also has something to offer the reproducibility debate. As described in the Introduction, actually reproducing studies requires more than reading methods (from historians of science and statisticians Porter 1996, Stodden et al. 2014, Stark 2018). The technical aspects of reproducibility are challenging enough, and many researchers are working to make this easier (see environmental scientists Boettiger et al. 2015, Kitzes et al. 2017, Ram et al. 2018) -- however there is a level at which replication may be theoretically impossible because the researchers, the place, and the time are all different from the original. Therefore, more broadly, we could re-frame scientific reproducibility as a triangulation process, explicitly acknowledging that each study is necessarily different. Then, if the aim is to replicate results, at best a study may only be partially confirmatory of the original.

Multiple stories are another way to reframe modeling uncertainty, as well. Environmental scientists Petersen et al. (2011) outline several different attitudes towards uncertainty: the "deficit view" in which uncertainty is a number to itself be quantified, reduced, and eventually eliminated; the "evidence evaluation view" which "focuses on generating robust conclusions and widely shared interpretations of the available limited knowledge" through building scientific consensus; and finally, the "post-normal view" (drawing on the definition of "post-normal science" from philosophers of science Funtowicz and Ravetz 1993), in which uncertainty is an inherent property of complex systems. Anthropologist Mol (2002) describes this last view as the "permanent possibility of alternative configurations," calling the potential for different ways of defining and viewing a scientific object "doubt." However, embracing uncertainty does not prevent us from acting: Mol reassures us that "the permanent possibility of doubt does not lead to an equally permanent threat of chaos" and that "open endings do not imply immobilization." Futhermore, modeling practices (and many other research processes) benefit from recognizing that "capacity-building in the face of uncertainty has to be a multidisciplinary exercise, engaging history, moral philosophy, political theory and social studies of science, in addition to the sciences themselves" (from science and technology studies scholar Jasanoff 2007). Therefore, rather than viewing model uncertainty as a problem, we could view it as an opportunity to engage stakeholders and interdisciplinary teams, an invitation to study a particular aspect of a system more closely, or a call to triangulate the analysis with other knowledge. Petersen et al. (2011) also suggest that their three views of uncertainty are complementary and not mutually exclusive.

Uncertainty could guide us towards what to investigate further (whether quantitatively or qualitatively). For example, sensitivity analysis in population viability models involves investigating which demographic parameters like growth or survival have the largest impact on overall population growth or decline. The results of the sensitivity analysis can therefore point to biological quantities that are important to know precisely in order to accurately assess population viability. If these parameters are not well known, the analysis helps direct research priorities towards better constraining them (see mathematical ecologist Caswell 2001). We could construct models like these that invite us to find those places to look for more information (this is closest to Petersen et al.'s "deficit view" of uncertainty), or point us to places where we should work especially carefully to triangulate with different datasets and knowledges (similar to their "evidence evaluation view"). There are also established methods for summarizing the certainty of meta-analyses, especially when decisions must be made based on this collection of evidence -- for example Grading of Recommendations Assessment, Development and Evaluation (GRADE) in medical studies (Meader et al. 2014).

Engaging multiple interested parties with an attitude of openness and a knowledge that we may not be able to resolve the uncertainty but must act anyway (the "post-normal" view) might be a way forward for some of difficult contemporary issues of global concern (see geographers and environmental scientists Voinov et al. 2016). We could seek ways in modeling and writing to quantify uncertainty, explore consensus, and recognize that we may not be able to resolve some things – and to take an attitude of opportunity and openness in this process. Particularly in situations where we have smaller amounts of data, rather than viewing the lack of predictive power as a problem, perhaps we can use the situation as a starting place for a conversation between different "stakeholders" or people who care about the topic. For example, collaborative modeling under uncertainty can include scenario modeling with expert knowledge elicitation (Voinov et al. 2016). And though communicating uncertainty to the public while maintaining a sense of authority is tricky, this kind of transparency is critical in establishing (or renewing) trust (economist Shafik 2017).

Seeking interdisciplinary training and experience is key in working on critical contemporary problems (potentially assisting with triangulation, according to epidemiologists Munafò and Smith 2018, and increasing statistical power, according to statisticians, epidemiologists, and psychologists Munafò et al. 2017). Choosing to do interdisciplinary work is also a prescription for encountering differences in epistemology, vocabulary, and research norms (see environmental scientist and ecological economist Lélé and Norgaard 2005) -- meaning that having ways to manage the experience of difference is also important. Difficulties can arise from differences in ways of knowing, recognizing and incorporating values into research, defining the questions of interest, and referring to the objects of study, among many others (Lélé and Norgaard 2005). Learning how to talk to each other is a first step in what geographer Wyly (2009) calls "trust" or "deference" between specialists which can allow for effective collaborations. One specific challenge when communicating between disciplines is working with differing vocabularies. For example, the term "community" is used differently by ecologists and participatory action researchers. Usage of discipline-specific technical terminology should always be accompanied by a willingness to slow down and clarify, whether it is an unusual (geology: "terrane") or common term with a particular meaning (statistics: "estimating," sociology: "bracketing").

In learning interdisciplinary collaboration skills, practical experience is key, and explicit training can be invaluable (see interdisciplinary scholars Oberg 2011, Science & Justice Research Center (Collaborations Group) 2013, Andrade et al. 2014) allowing us to challenge our ideas of how we know what we know (epistemology) and why we ask the questions we do -- within the context of a safe space where collaborators can admit ignorance and potentially encounter difficult reactions within themselves and in others (Science & Justice Research Center (Collaborations Group) 2013, Andrade et al. 2014). Then, when one later encounters differences while working in intellectually diverse groups, one has some training to fall back on to manage those experiences. Interdisciplinary work can involve more time and effort than monodisciplinary work, but it opens up many possibilities -- and training in how to do this work supports many of the other practices outlined here.

Interdisciplinary collaboration should also enable modelers to address ethical issues at all stages of a project, either by collaborating with ethicists (see biologist and statistician Boden and McKendrick 2017), or by being more aware of ethical issues themselves; to triangulate between different kinds of knowledge; to do ethnography of the datasets they use if the data biographies are incomplete, or to work with ethnographers who can do so; to work with science and technology studies scholars in writing data biographies; and to work more effectively in collaborations outside the academy when in search of more just and democratic modeling processes. Often, where discipline and epistemology differ, interest in or commitment to a particular place, people, technology, or issue can bring colleagues together (Science & Justice Research Center (Collaborations Group) 2013). Another key point for interdisciplinary collaboration is to include people with different backgrounds from the start: Rather than bringing a statistician in to analyze data after it is collected, or a social scientist in to bring a "social perspective" after the study is already designed, involve everyone in the project from the beginning (Andrade et al. 2014).

As we collectively and mindfully re-imagine contemporary modeling practice, we need interdisciplinary teams of practicing modelers and critical scholars. We can strive to be patient with different timelines and create an environment of mutual respect and trust where collaborators are able to admit ignorance and ask questions. These practices can be fostered at multiple levels, by both institutions and individuals.

The ways in which different collaborators' relative circumstances emphasize their knowledge production over others can be critical in the success or failure of research. Feminist studies scholar Haraway (1988) suggests that "we need... the ability partially to translate knowledges among very different -- and power-differentiated -- communities." ("Power" here refers to the critical theory sense of the word, not "statistical power" which has a specific mathematical definition, see above). For example, philosopher Fricker (2007) describes epistemic injustice as "a wrong done to someone specifically in their capacity as a knower," and details a kind of testimonial injustice in which prejudice causes some peoples’ knowledge to have lesser credibility than others. Unfair treatment in knowledge production can relate to financial resources, workload, authorship and credit, among other dimensions. Power imbalances can arise between academics from different disciplines (in which some are marginalized due to normative assumptions about rigor or usefulness, see environmental scientist and ecological economist Lélé and Norgaard 2005), between individuals with different job titles or seniority levels, and between academics and communities they work with (where communities may be at a disadvantage due to perceived academic authority).

For example, in writing and working on interdisciplinary grants and projects, collaborators from different disciplines do not necessarily receive equal financial (and other) benefits. Even within a discipline, the majority of the labor can often be pushed onto less senior, more vulnerable team members (e.g. graduate students and postdoctoral researchers). Ensuring that greater labor and responsibility comes with appropriate authority, compensation, and credit could help to mitigate this imbalance. Even in explicitly participatory research, many authors do not give authorship credit to the communities they engage with (see interdisciplinary scholars Sarna-Wojcicki et al. 2017). Authorship, along with other benefits of the research, should be explicitly discussed with community partners. In addition, sensitivity to labels and terminology can be important (see interdisciplinary scholars Eitzel et al. 2017): some terms can marginalize collaborators because of pre-existing systemic or structural disadvantages. Attention should also be paid to specific histories of injustice, for example colonial histories and negative past relationships between Indigenous people and universities.

Learning to look through different lenses and to perceive the impacts of power differentials between collaborators is not typically part of modelers' training, but modelers can learn to facilitate discussions of these issues with colleagues -- explicit facilitation training could be useful here. Even being aware of or receptive to hearing about problems can be an important first step, and documenting these issues in the data biography may also be appropriate. Modelers and their collaborators may need to experiment with ways to mitigate power imbalances, even potentially pushing back on institutional or structural barriers that constrain them, where possible.

Modeling does not take place in a vacuum, and engaging with its justice implications should involve asking who will be harmed and who will benefit from models, or as political ecologists might put it, who are the "winners and losers?" (Robbins 2011). Feminist studies scholar Haraway (1988) says that "feminist objectivity ... allows us to become answerable for what we learn to see." Therefore, we should be responsible for being aware of and open to our own relationships to what we study, understanding how we are located in a web of power relations, and to be accountable for what we observe, including its impacts and implications -- to be "noninnocent" as Haraway puts it. We can strive for our science to be "response-able" (i.e. able to respond to the situations it uncovers or finds itself intentionally or unintentionally embedded in, sociologist Reardon 2013). Further, if we want to do what sociologist Thompson (2013) calls "good science," we need to do science "with" ethics, in which ethical, legal, and social implications of research are examined when research is planned and in process, not just as an after-the-fact or downstream step (Raji et al. 2020). Frequently, people who should benefit from the production of knowledge and have a hand in directing it are shut out of the process and potentially harmed by it (see sociologist Mah 2017). How can researchers engage with the political contexts of inequities that their work may exacerbate? While we cannot completely control how the world will use our work, that does not absolve us of responsibility in considering these questions before and during our research processes (see economists and biologist and statistician Derman and Wilmott 2009, Boden and McKendrick 2017). There can be a sense that scientists should stay out of politics in order to be "disinterested authorities" -- but feminist objectivity already points out that we are not objective in the sense of "seeing from nowhere" (Haraway 1988). We always see from somewhere. The real way to be objective is to check ourselves against others. Fortunately, according to historian and philosopher Gieryn (1999), the boundaries of science are flexible and dependent on context, which means we can choose to redefine them to include ethics. This is another form of context which should not be ignored; instead, we should go looking for the ways people might use our work and how this might impact vulnerable populations (see biologist and statistician Boden and McKendrick 2017).

Mixed-method qualitative ground-truthing of models along with open access to their assumptions and mechanisms can be key for being responsible for the models' impact; if they begin to do damage but people can audit them, the system can auto-correct (see mathematician and economist O'Neil 2016). Specifically with respect to big data justice, researchers have begun exploring methods for auditing algorithms (communications and legal scholars Bodo et al. 2018), and recently Access Now and Amnesty International (2018) have created a Declaration "Protecting the rights to equality and non-discrimination in machine learning systems." Modelers and organizations can subscribe to this declaration and others like it as part of a commitment to working with the impacts and implications of their work. Interdisciplinary collaboration with critical scholars can also help modelers to investigate potential benefits and harms when designing and implementing projects.

Where possible, modelers can work directly with the people who will be impacted by their results (though one challenge is identifying not just intended end-users of models, but others who will be impacted as well). Mathematician and economist O'Neil (2016)'s examples of algorithmic injustice are so damaging because the algorithms are proprietary and therefore not accountable to those that they harm. Re-imagine O'Neil's recidivism example if the modelers engaged with incarcerated people to find out what interventions or information they thought would keep them from returning to prison when designing models that represent the relationship between the length of prison sentences and the chance of being imprisoned again later. Finding ways to solicit user input on the results of predictive algorithmic models, which could then feed into the improvement of the algorithms, could be a way to reduce the harm done by these models. Even better than holding black-box models accountable, however, is creating models in partnership with the people they will be applied to from the start; environmental scientist Étienne (2013) calls this "companion modelling." This kind of modeling enables researchers to create "better accounts of the world" (feminist studies scholar Haraway 1988). Often the community's knowledge is complementary to an outsider researcher's, and together they can achieve a better representation of the system in question (sociologist Fortmann 2009). Anthropologist Lansing (2009)’s understanding of Balinese water temples benefited immensely from working directly with local community members, including religious leaders. Receiving community input while developing a model and letting users' feedback drive model validation (see sociologist Saam 2019) can therefore improve model quality.

Collaborative modeling also engages people with the modeling process and products and invites the opportunity for more just modeling. This approach incorporates some of the concepts of Participatory Action Research (PAR), in which research is "generally not done on participants; it is done with participants" (educational scholars Merriam and Tisdell 2015) and "seeks to develop and maintain social and interpersonal interactions that are nonexploitive and enhance the social and emotional lives of all people who participate" (participatory scholar Stringer 2013). Therefore, participatory modeling can be one way to bring together multiple partial knowledges in a more just way. The community could even direct the modeling process from the beginning, focusing on the questions that are most important to them, working with data they collect, and validating and analyzing the results with them as well (interdisciplinary scholars Eitzel et al. 2020a, Eitzel et al. 2020b). Communities ideally should be involved in ongoing future developments and applications of the model, as well (interdisciplinary scholars Eitzel et al. 2021).

While it may not be feasible to engage with users during all phases of modeling, making an effort to increase engagement where possible could be a way to improve modeling transparency and trustworthiness. Many modelers are encouraging working with model users (see mathematicians, geographers and environmental scientists Jakeman et al. 2006, Voinov et al. 2016) whether this means decision-makers (e.g. complexity modelers Grimm et al. 2014), journalists (e.g. statistician Spiegelhalter 2017), or others. As this trend continues, the modeler's role as an expert may shift, but perhaps this is ultimately beneficial: according to geographer Lave (2015), "we might achieve more of our political and intellectual goals by embracing the progressive aspects of our reduced authority than by fighting its erosion." If modelers want to improve equity in modeling, we may need to face a future scientific process in which we are not the gatekeepers of knowledge production.