# Causal Graphs and Concept-Mapping Assumptions
## 2 Literature Review
This is done to solve problems that are common to these stakeholders by unifying, standardizing, and connecting the information that is presented by and for the stakeholders in question [27; 29]. While this is a useful technique for helping groups understand problems from many different angles, it is not a generalized way of inferring causality and truth. Creating and testing different causal graphs with the evidence that is available is a separate process that aims to produce general knowledge of empirically inferred reality. The goal with causal graph analyses is to produce a coherent and accurate map of a given concept or problem that is more probably true than competing alternative maps. It is the process of weeding out models that are not supported by evidence, more than it is just the production of different models. Most people have implicit assumptions about how the world works, in addition to possible desires about how they would like the world to work [15; 30; 31; 32]. One method for determining the underlying assumptions that are implicit in a research project is to map them out through a process known as \"cognitive mapping\" [33; 34; 35; 36; 37]. Cognitive mapping has been used to understand the implicit assumptions and decisions made by policymakers in the past [38]. Cognitive maps give rise to different concept maps, which then are used to produce different causal graphs. It is logically plausible that the creation of causal graphs in causal modeling is produced by the conceptual maps people make by the same cognitive maps used by researchers, policymakers, and stakeholders. Indeed, cognitive mapping is implicit in some research concerning Bayesian networks, which map out the probabilities that a set of causal conditions relates to a set of observed variables [39; 40; 41; 42; 43]. It also has been linked to modeling ecological systems by researchers [37]. The hypothesis that underpins this perspective is that implicit cognitive maps of researchers, policymakers, and stakeholders alike result in the production of different conceptual maps of the world. The interplay between cognitive maps and conceptual maps gives rise to different causal graphs being produced through the different perceived factual \"nodes\" (points) and connecting \"edges\" (relations or connections between factual nodes) of the researcher, policymaker, and stakeholder. This is different from existing methods for making the goals of researchers, policymakers, and stakeholders explicit, such as the Logic Model. Logic models display the connection between different inputs and activities with different outputs and outcomes [44], in that this method is more free-form and allows the cognitive maps and implicit biases to be made more readily apparent instead of confining the maps to a preset form. Different assumptions, perspectives, levels, and degrees of awareness in the cognition of researchers, policymakers, and stakeholders result in the perception of different \"facts\", different interpretations of those \"facts\", and different edges among the \"facts\". This could be done implicitly and subconsciously by the researcher and policymaker, but it also is hypothetically possible for it to be done deliberately through conscious choice and selection of facts, interpretations of facts, and edges among facts [45; 46; 10; 32]. A hypothetical example of this is between people who identify as \"conservatives\" and \"liberals\" looking at the same situation facing their shared nation. The conservative may claim that the moral integrity of the society is eroding as time passes, while a progressive may have a different outlook on change and difference in a society from one time to another. The evidence suggests that people on both sides will look for, perceive, and interpret the situation differently in mutually exclusive way. For example, the environment cannot both be and not be affected by humans' economic activities, and it cannot both be and not be significant for human survival.