Chapter 7

Points of View, Assumptions, Beliefs

We become not a melting pot but a beautiful mosaic.

Different people, different beliefs, different yearnings,

different hopes, different dreams.

- Former U.S. President Jimmy Carter

Jimmy Carter, the 39^th U.S. president, highlights how different people have different beliefs. He also points out how the United States is not a “melting pot,” as many of us learned in school, where residents are assimilated into one seamless culture. Instead, he offers there is a U.S. “beautiful mosaic,” meaning the United States is covered with a host of different peoples, beliefs, and cultures.

This chapter details how thinkers can assess the many influences that lead to different beliefs and how to use these assessments in a critical-thinking project. The material in this chapter takes an agency approach, meaning it looks at how individual agents or groups of agents making decisions are influenced by several factors. Based on an agent’s “mental model” they develop perspectives or their lens on the world. These perspectives then influence their decision-making and behavior. Chapter 6 reveals the different political cultures that influence a country or societal group’s political, economic, and social conditions and overall perspectives. In this chapter we discuss techniques for assessing agent’s perspectives in terms of their points of view, assumptions, and beliefs. These items must be assessed not only for agents making decisions or acting on a thinking-project issue, but also for the thinkers conducting the analysis. Points of view and assumptions speak to the belief systems of the agents under study, and include structural historical, ideological, political, economic, social, cultural, religious, linguistic, and security factors. Decision-making is also influenced by an agent’s emotions. Evaluating points of view, assumptions, and beliefs are crucial for analyzing agents’ decisions and behaviors, a key component of critical thinking.

Analyzing Perspectives

Figure 7.1 depicts an abstract human mental model for how information is processed resulting in an agent’s perspectives leading to their points of view, assumptions, and beliefs.^[1] The model is abstract as it does not depict reality but helps us understand the many influences creating a person’s perspectives. The model offers how information and logic leading to knowledge possessed by the agent (covered in Chapters 3-6 for civic life) is inserted into a vessel (here a funnel) containing numerous different filters. The filters impact (influence, affect, etc.) the information, logic, and knowledge, ultimately resulting in the funnel’s output of an agent’s lens on the world (perspectives) that help define the agent’s points of view, assumptions, and beliefs in the situation under study. Figure 7.1 highlights the complexity of human thinking.

The upper right side of Figure 7.1 indicates a political culture filter that influences how the agent either operates within their existing culture or strive to attain one they want to bring about. This filter creates a larger context for the environment in which the agent operates. Because of the heavy influence political culture can have on an agent’s perspectives it is placed as the first filter at the top of the funnel. Political culture may include the agent’s country’s political culture, other political divisions’ political cultures (state, county, city, etc.), or their societal groups’ (organizations) political cultures affecting the agent’s thinking—depending on the issue being analyzed. See Chapter 6 for details on identifying and assessing different political cultures.

Filters on the left side of Figure 7.1 highlight additional influences on an agent’s perspectives. These filters are very specific to individual agents. The analysis of these filters normally starts with a psychobiography, an analytic process mainly conducted by psychologists, psychiatrists, criminal profilers, intelligence analysts, and foreign policy analysts that are informed by theories of cognitive psychology. For thinkers who are not familiar with psychobiography, it begins with the critical-thinking information search for data (Chapters 3-6) requiring the thinker to collect information on agents, i.e., specific individuals or groups of individuals empowered as decision-makers in a situation. A psychobiography is a combination of assessments that detail specific leader and decision-maker characteristics influencing their perspectives, including biographic background (where born, family experience, education, religion, etc.), world experiences, cognitive and reasoning abilities, physical and mental states, attitudinal approaches, leadership and personality traits, and outside or group influences on the leader or decision-maker. Describing specific techniques for assessing each of these areas is beyond the scope of this book.^[2] Additional information on conducting a psychobiography is contained in Chapter 6 of Security Analysis, A Critical-Thinking Approach.^[3] The current situation and the agent’s propensity to employ cognitive biases (heuristics) and logic fallacies in their thinking are also part of the left side filters. Chapter 4 discusses identifying cognitive biases and logic fallacies.

On the lower right of Figure 7.1 is the important emotions filter. This filter is where agents tend to use opinions and feelings to mold their perceptions. The source of opinions mainly come from the Figure 3.2 knowledge categories of authority, faith, common sense, and intuition. Agents using mainly emotions to make decisions tend to disregard the Figure 3.2 higher-level knowledge categories of empiricism, rationalism, and science. In other words, System 2 (slow) fact and logic based rational thinking is replaced by System 1 (fast) thinking driven by emotions and feelings. Agents basing their perspectives and decisions on emotions or feelings are often little-informed or misinformed about the situation.

Emotions can have both positive and negative effects on an agent’s perspectives and feelings. Typical emotions or feelings at play include:

Happiness

Excitement

Joy

Love

Enjoyment

Calmness

Amusement

Anticipation

Empathy

Anger

Fear

Anxiety

Revenge

Sadness

Disgust

Pride

Remorse

Shame

Disappointment

Embarrassment

Envy

Guilt

Jealousy

Annoyance

Contempt

Doubt

Surprise

Emotions can take over a decision process, especially in System 1 (fast) thinking efforts (see Chapter 4). Normally lacking complete truthful information, the agent tends to form strong opinions reinforced by confirmation bias (see Chapter 4). It is extremely hard to change an opinion reinforced by a stream of misinformation, emotions, and feelings. This helps answer the question of “Why do agents act against their own best interests?” Many low (and some moderate) information people (agents) create their perspectives and make decisions with an avalanche of misinformation, often strengthened by the repetition informal logic fallacy and confirmation bias and without assessing the consequences of their decisions (see Chapter 2). This sort of decision-making often acts against the agent’s own best interests.

The thinker can assume when using Figure 7.1 that the agents under study are involved in System 2 (slow) thinking (Chapter 4). This assumption is invalid for low information people and others basing their decisions mainly on emotions. When the agent is involved in a System 1 (fast) thinking effort, some of the filters in Figure 7.1 will likely not come into play, while others may still influence the final decision, but opinions driven by emotions and feelings may be the strongest influences. To facilitate analyses based on Figure 7.1, thinkers should prepare a pre-analysis psychobiography on agents they commonly encounter or expect to encounter in their thinking projects. Also, do not forget thinkers need to complete a psychobiography on themselves so their biases do not adversely affect the thinking-project findings. Next, we turn to techniques for further assessing perspectives related to points of view, assumptions, and beliefs.

Analyzing Points of View

Identifying points of view on issues or situations is often difficult. This is the first step in assessing agent perspectives. A good starting point for assessing points of view is to use a Four Ways of Seeing template. This technique for identifying different agent “broad” views of an issue or situation has been used in the intelligence, security, and critical-thinking communities for many years.^[4] It allows the thinker to delineate differing points of view and help tease out key assumptions for one or more agents by using a simple matrix technique shown in Figure 7.2. The blocks for two agents list their views of both the issue at hand and their views of the other agent.

Often, there are more than two agents involved in a thinking project. Figure 7.2 defines a dyadic (two-agent) scenario that is flexible and may be modified to include all agents involved in the issue. In a situation with only one outside agent (A), the second agent (B) could capture the views of the thinker. If there are more than two agents, then the figure would be expanded to Nine Ways of Seeing (3 agents), Sixteen Ways of Seeing (4 agents), and so on. When there are more than two or three agents, a better analytic technique is to conduct a pairwise comparison, where the Figure 7.2 dyadic model would be used to analyze each agent individually against each other agent. The analysis can get complicated with more than two or three agents. Remember the goal of the Four Ways of Seeing technique is to identify the broad “big picture” points of view of the agents involved in the thinking project. As an added result, Four Ways of Seeing will start identifying key assumptions (discussed in more detail below).

While a single thinker may complete Figure 7.2, it is better to have groups of thinkers work together as it is useful to bring more than one analytic perspective to the thinking project. Additional thinkers help insert differing perspectives into the thinking process. On a large thinking project, these additional thinkers should ideally differ in experiences, educational backgrounds, cultures, technical knowledge, or mindsets. Thinkers, whether individually or in groups, normally engage in a process best described as informed brainstorming,^{[5] [6]}which entails a number of techniques to generate alternatives, such as alternative points of view. As thinkers consider the information collected on the situation (Chapter 3-6) and the results of the Four Way of Seeing assessments and assumption analysis in the next section, plus any informed brainstorming results, they should also ask:

Why (am I/are we) confident the points of view or assumptions were correctly identified?

In what circumstances might points of view or assumptions be overlooked?

Could the points of view or assumptions have applied in the past but are no longer applicable today?

If the points of view or assumptions turn out not to be applicable today, how much impact would including them have on the analysis?^[7]

For an example of Four Ways of Seeing, we will employ a specific research question continuing from the Figure 2.10 example for the specific research question about “Which U.S. immigration policy should I support?” The Figure 2.10 analysis allows the thinker to determine their own initial view of the immigration issue. In Figure 7.3 a hypothetical Four Ways of Seeing example (here a Nine Ways of Seeing) is shown, which identifies multiple agent’s views of immigration. We assume in this example that the thinker is analyzing the immigration views of three political candidates (agents) for a U.S. presidential election. Figure 7.3 candidates are shown with their likely political cultures (Chapter 6). This analysis assists the thinker in eventually selecting the candidate with the view of immigration policy that most closely matches their own (see Chapter 8).

Hypothetical Information

Analyzing Assumptions

The second step in assessing agent perspectives is to uncover the agent’s key and supporting assumptions. Identifying and assessing assumptions are critical parts of a thinking project.^[8] The above points of view analysis may uncover some agent assumptions, but a deeper investigation to identify additional assumptions is likely still needed. This is often difficult as agents do not readily reveal their assumptions. The thinker must look beyond the visible facts of the situation and seek assumptions that undergird the thinking or behavior about the issue or situation under analysis. For example, as highlighted in Chapter 6, those agents working from a liberal ideological view assume “Countries are not rational actors, i.e., country decision making is really a complex mix of coalition and counter-coalition building, bargaining, and compromise, which might not lead to optimal decisions.” In other woods, agents following a liberal ideology would see immigration as an issue where coalition building, bargaining, and compromise could lead to solutions (although the solutions might not be optimal). Thus, when analyzing leaders or decision-makers working from an egalitarian or individualistic political culture, this underlying assumption will usually be at work, even if not spelled out by the leader or decision-maker. One method to identify underlying assumptions is to simply ask, “What must be true for this situation to exist?” Or “What would need to change for the opposite of this situation to occur?”

In social science, assumptions usually define theoretical propositions accepted as true even though they cannot be proven or disproven by empirical data or logic. Social science also identifies assumptions without empirical proof as used by thinkers to make inferences leading to their thinking-project’s findings and conclusions. U.S. critical theorist Stephen Brookfield uses a broader definition of assumptions whereby they are any conception (theories, facts, perspectives, worldviews, points of view, beliefs) used in decision-making. Brookfield offers that once assumptions are identified they should be categorized as to their main sources as either paradigmatic, prescriptive, or causal (see details below).^[9],[10] These categories may be used to classify both key and supporting assumptions.

Brookfield highlights how assumptions operate as instinctive guides to behavior, which is something people seldom consider because assumptions reside deep within a person’s mental model (Figure 7.1). Assumptions influence how people think about the situation at hand. Brookfield offers they are difficult to evaluate as right or wrong, or valid or invalid, especially paradigmatic and prescriptive assumptions, but should be considered as to whether they are contextually appropriate for the situations they govern. Brookfield also boasts that identifying and categorizing assumptions is all that is needed in a critical-thinking analysis, although this book does not make that same leap of faith. Brookfield developed the following categories of assumptions:

Paradigmatic assumptions concern the deeply held assumptions that frame an agent’s views of how the world works; in other words, the agent’s “dominant personal ideology.” Paradigmatic assumptions go to the heart of an agent’s points of view or personal belief system and include political, economic, religious, cultural, and social aspects of how the agent views the way the world works. These assumptions usually spring from dominant ideologies (see Chapter 6). For example, the dominant ideologies of democracy and free-market capitalism are so pervasive in egalitarian societies, their core assumptions often are never questioned in analyses. These core assumptions normally are accepted as the common-sense way of organizing and operating in the world. Paradigmatic assumptions often are hard to uncover, especially by analysts whose thinking is influenced by the same paradigmatic assumptions.

Prescriptive assumptions concern those that define the desirable ways an agent thinks or acts. They are mainly normative because they define what “ought” or “should” be the desirable ways of thinking or acting and how the world “ought to” or “should” work. Prescriptive assumptions tend to flow from an agent’s ideological paradigmatic assumptions of how the world works. For example, prescriptive assumptions might define how a democracy should function or how resources ought to be allocated in a capitalist free-market system. In addition to flowing from an agent’s paradigmatic assumptions, prescriptive assumptions result from the structure of social rules (laws, regulations, policies, norms, treaties. etc.) telling people how they should act. There are many social rules (both formal and informal) that influence thinking and behavior and can be categorized as prescriptive assumptions.

Causal assumptions concern theoretical (cause and effect) and evidence-based assumptions about how the world works. Causal theoretical assumptions are generally statements or hypotheses about how one or more variables cause changes in another variable. In social science, the basic form of causal assumptions entails propositions or hypotheses stating how human thinking, decisions, behaviors, or conditions in factor X, results in or cause a change in human thinking, decisions, behaviors, or conditions in factor Y (i.e., the issue under study). In the physical sciences, causal conditions are much easier to assess as they have been established through repeated observation (experiments) and analysis. In the social sciences, causal conditions are much more elusive because of the complexity of human behavior and the lack of research in many aspects of social behavior. Causal assumptions related to social science often are deemed suspect because of the small sample size of cases governing the proposed causal linkages. Just because one agent or a small group of agents think or behave in a certain way, does not mean their thinking or behavior can be generalized to the thinking or behavior of a larger group of agents (part-to-whole informal logic fallacy). Further, just because an agent behaves in a certain way in one situation does not mean they will behave the same way in future situations (weak analogy informal logic fallacy). Causal assumptions also may offer statements of information (data, facts, evidence). Unfortunately, it is common to find causal assumptions offered in statements or arguments with rampant misinformation and bad logic and reasoning (see Chapters 3-6). This highlights the importance of consistently checking the accuracy and validity of information and logic presented as causal assumptions (see next section on belief analysis).

Most issues or situations are governed by a mix of the above three assumption categories—paradigmatic, prescriptive, and causal. Normally, approximately 80 percent of assumptions in an issue or situation are causal in nature.^[11] Thinkers may need to look deeper to uncover ideologically influenced paradigmatic and prescriptive assumptions. This effort may cause additional revisions to the list of assumptions generated in the Four Ways of Seeing and informed-brainstorming efforts.

To continue our Figure 2.10 example about the research question “Which U.S. immigration policy should I support?” and determining the immigration policy views of our hypothetical U.S. presidential candidates (see Figure 7.3) we must conduct an assumptions analysis of the issues. First, we must identify the key and supporting assumptions for each candidate (agent) involved. This is done through our thinking project’s information search, candidate written and verbal statements, Figure 7.3 analysis, informed brainstorming, and other research. Second, we classify each assumption based on Brookfield’s above categories. Third, we assess the validity of each assumption. Paradigmatic and prescriptive assumptions are normally “undetermined” as we cannot determine if they are right or wrong (valid or invalid). Causal assumptions can be found valid or invalid using the belief analysis techniques in the next section. Figure 7.4 provides an assumptions analysis for our example on immigration views.

Hypothetical Information/*Validity determined by Belief Analysis (see below).

Figure 7.4 provides the thinker with a better understanding of each candidate’s (agent’s) perspectives by identifying the web of beliefs of key and supporting assumptions influencing each candidate’s thinking concerning immigration views. Figure 7.4’s first two columns identify specific assumptions and their assumption categories based on Brookfield’s work discussed above. The third column provides space to record each assumption’s (belief’s) validity assessment. Determining an assumption’s validity is the topic of the following belief analysis discussion.

Assessing Beliefs

Belief analysis is the third and final step in assessing agent perspectives. One definition of belief entails “any conception that, if affirmed, shapes human experience, emotion, understanding, judgment, or action.”^[13] In this broader context, beliefs may include an agent’s worldviews, perceptions, points of view, and assumptions, in other words the outputs of the Figure 7.1 Human Mental Model. The basic purpose of belief analysis is to determine if the belief is valid or invalid, then based on this determination develop the implications and consequences of policies implemented or actions taken based on the belief.

There are two steps in basic belief analyses. First, thinkers determine the validity of a belief employing critical-thinking-related techniques in Chapters 3 to 6. Second, the validity findings may then be used to predict the likely success or failure of policies or actions based on those beliefs. Belief analysis offers valid beliefs will more likely result in successful policies and actions. Conversely, it offers invalid beliefs will more likely result in unsuccessful policies or actions.^[14] Determining belief validity and if acted upon its likely success is the minimal effort required in a critical-thinking belief analysis. A more complex approach to belief analysis is found in Critical Belief Analysis for Security Studies.[15]

In Figure 7.4, causal assumptions are assessed as either valid (Yes) or invalid (No) based on truthful information, good logic, and suitable reasoning using critical-thinking assessment techniques. Assumptions could be further classified as valid (Yes) or invalid (No) with Caveats, indicating changes to conditions (facts, logic, or reasoning) that could make the assumption’s validity assessment change from a No to a Yes (or vice versa). We do not use caveats in this chapter’s example.

In continuing our analysis from Figures 7.3 and 7.4 of the example research question, “Which U.S. immigration policy should I support?” we employ belief analysis to assess the validity of each candidate’s beliefs. Our analysis assumes that all three presidential candidates are vulnerable to bias in their selection and interpretation of information. We assume the material that shapes their views are also subject to inadvertent bias. Chapters 3 and 4 discuss in more detail biased information.^[16] Reviewing Figure 7.4 reveals all three candidates view illegal immigration negatively, undocumented immigrants should be deported, and both undocumented immigrants and legal immigrants committing crimes should be deported (the so-called common ground). A belief analysis should be conducted on each of the candidates’ key and supporting causal assumptions (beliefs) listed in Figure 7.4, but such a lengthy analysis is beyond the scope of this chapter. We will assume these assessments were conducted and the results shown in Figure 7.4. For learning purposes the following discussion focuses on the candidates’ differing views of the key assumption on whether immigration does or does not raise U.S. crime rates.

Candidate A. Working from an egalitarian approach, Candidate A believes that immigration does not raise U.S. crime rates over crime rates of U.S.-born citizens. If their immigration and crime belief is deemed valid, policies and actions inspired by Candidate A’s view of immigration and crime are likely not to be a threat to the United States. This is consistent with Candidate A’s egalitarian political culture focused on promoting the good of the entire society.

Candidate A’s view of immigration and crime is supported by numerous governmental, academic, and left/centrist-leaning pro-immigration think tank studies (see Figure 3.5). Using government statistics, survey data, and other social data, these sources develop a broader nation-wide “big picture” of immigration effects on the United States showing national trends through crime and other social data.^[17] These studies conclude that immigrants do not present a threat to the United States. They highlight that both legal and undocumented immigrants commit crimes well below the crime rates of U.S.-born citizens.^[18] If we assume that these studies meet professional research and analysis standards, their guidance appears valid.

Since the other Figure 7.4 causal assumptions (beliefs) for Candidate A all possess valid (Yes) evaluations, their belief analyses are assumed similar to those for Candidate A’s immigration and crime belief. The expanded belief analysis for Candidate A reveals that policies or actions based on Candidate A’s Figure 7.4 web of beliefs on immigration should likely result in success.

Candidate B. Working from a mixed egalitarian-individualist approach, Candidate B has a similar view to Candidate A on most immigration issues in Figure 7.4. However, unlike Candidate A, Candidate B believes that immigrants, both legal and undocumented, commit crimes at higher rates than U.S.-born citizens. As such, Candidate B advocates devoting additional attention and resources to addressing immigrant criminality. This is consistent with Candidate B’s individualistic beliefs in their mixed egalitarian-individualistic political culture where security is the dominant interest.

Candidate B’s immigration and crime belief is deemed invalid because it is not based in valid crime and social data but instead is supported by mainly anecdotal (specific case) reporting spread via the repetition informal logic fallacy by conservative politicians, right-leaning anti-immigration think tanks, and right-wing media, especially social media.^[19] For example, if one undocumented immigrant commits murder, the “spin” in the reporting on this anecdotal incident usually implies all undocumented immigrants are murderers. Such reporting concludes that immigrants committing crimes present a threat to the United States and its citizens. Anecdotal evidence to support the belief violates the law of small numbers and representativeness cognitive biases, plus the part-to-a-whole, appeal to fear, and stereotyping informal logic fallacies (see Chapter 4).

Candidate B’s belief likely leads them to offer immigrant criminality should inspire immigration enforcement policies leading to demonstrably positive results. However, being invalid, Candidate B’s view of immigrant criminality is likely to be misleading or ineffective. In other words, Candidate B’s belief could inspire immigration enforcement policies or actions likely to be useless or destructive.

The additional Candidate B causal assumption assessments in Figure 7.4 are similar to Candidate A’s and possess valid (Yes) evaluations. This predicts policies or actions based on Candidate B’s web of beliefs on immigration, less the immigration and crime belief, would likely result in success.

Candidate C. Working from an individualistic approach, Candidate C believes that immigration does raise U.S. crime rates over crime rates of U.S.-born citizens. Candidate C sees crime by both legal and undocumented immigrants as a significant threat to the United States and its citizens and significant enforcement resources should be dedicated to the threat. This belief is consistent with Candidate C’s individualistic political culture with a dominant interest in security.

Like Candidate B, Candidate C’s belief on immigration and crime is invalid as it is not based on valid crime and social data but instead is supported by anecdotal reporting by conservative politicians, right-leaning anti-immigration think tanks, and right-wing media, especially social media. This anecdotal reporting is spread by the repetition informal logic fallacy. Candidate C’s immigration and crime belief suffers from the same cognitive biases, informal logic fallacies, and invalid data and reasoning as does Candidate B’s above. Such invalid beliefs promote how immigrants committing crimes present a significant threat to the United States.

Like Candidate B, Candidate C’s belief guidance likely leads them to understand how immigrant criminality should inspire policies leading to demonstrably positive results. Unsurprisingly, the guidance inspired by Candidate C’s view of immigrant criminality is likely to be misleading or ineffective. In other words, Candidate C’s immigration and crime belief guidance could inspire policies or actions likely to be useless or destructive.

The additional Candidate C causal assumptions in Figure 7.4 possess both valid (Yes) and invalid (No) evaluations. The Candidate C invalid (No) causal assumption evaluations reveal that policies or actions based on Candidate C’s invalid (No) items, would also likely result in useless or destructive policies or actions. However, Candidate C valid (yes) evaluations reveal policies or actions on these assumptions would likely be successful.

The belief analysis results above should lead to the thinker conducting a self-analysis on their views of immigration issues and compare their results to the three presidential candidates. This allows the thinker to determine which candidate is closest to the thinker’s views and thus influence their ultimate decision of which candidate to vote for. This prepares the immigration issue for inclusion in the critical-thinking effort in Chapter 8. The analysis of the thinker’s own immigration views can start with the analysis in Figure 2.10.

The above belief analysis techniques can also help predict future actions of the agents assessed. We can expect Candidate A to use the information supporting their Figure 7.4 web of beliefs in their presidential campaign platform on immigration issues. Candidate A’s campaign should develop logical arguments their campaign should be expected to follow when investigating, contemplating, criticizing, discussing, or defending their immigration beliefs. For Candidate A, information for these arguments would be compiled from the government, academic, and think tank studies that support their beliefs’ validity. Candidate A’s arguments would encourage attention to comparisons of conditions between U.S. citizens and immigrants. These arguments should encourage Candidate A to view the harmful acts of immigrants similar to the harmful acts of U.S. citizens. These comparisons would be based primarily on System 2 (slow) cognitive (rational) thinking and focus on how undocumented immigrants do not commit more crimes per-capita than U.S.-born citizens. Candidate B’s campaign arguments would be similar to Candidate A’s, with the exception of the immigration and crime issue. Candidate C’s Figure 7.4 valid (Yes) causal assumption assessments would have similar campaign arguments as Candidate A’s.

Candidates B’s immigration and crime issue and all of Candidate C’s Figure 7.4 invalid (No) causal assumptions would have a different campaign platform approach on the immigration issue. Their invalid (No) arguments would be based on the anecdotal evidence broadcast by conservative politicians, anti-immigration think tanks, and media reporting discussed previously. They would also be supported by misinformation spread on right-wing social media. The invalid (No) arguments of Candidates B and C would try to cultivate an appeal to fear informal logic fallacy, hoping to impart fear of immigrants into the U.S. populace as they focused their attention on the deleterious effects of immigrant criminality and harm done by immigrants to life in the United States. These campaign arguments of Candidates B and C encourage them to view the criminal acts of immigrants as avoidable and the toleration of such acts as irresponsible. Both candidates would maintain such harm would not have occurred if its perpetrators had been barred from entering the United States or are deported when located in the United States. Since their invalid (No) arguments would be largely based on misinformation, the arguments would be the equivalent of candidate gaslighting campaigns (see Chapter 3). Another example of political gaslighting is found at the end of Chapter 4 concerning IRS budget issues.

It can be expected that all candidates will support their immigration beliefs (especially during an election campaign) based on their compiled campaign platform arguments. If a candidate can anticipate the arguments of the other candidates, they should be able to construct counterarguments to their competitors’ offerings. Finding ways to counter an opponent’s arguments is a common part of political discourse. Additionally, if elected, each candidate would likely act on their Figure 7.4 web of beliefs about immigration. This situation requires an implications and consequences element analysis described in Chapters 2 and 8.

Concluding Thoughts

Human thinking is complex. Figure 7.1 attempts to capture several important aspects of human thinking. The myriad of influences on peoples’ thinking leads to differences in each person’s world views (perspectives). This chapter provides techniques for assessing differing perspectives through analysis of a person’s points of view, assumptions, and beliefs. Such analyses are an important part of a critical-thinking project. In Chapter 8 we complete a critical-thinking project with an expanded research question of “Which Political Candidate to Vote For?” and where several major societal issues, including immigration, must be analyzed.

Notes

1. Adapted and modified from Valerie M. Hudson, Foreign Policy Analysis, Classic and Contemporary Theory 2^nd ed. (Lanham, MD: Rowman & Littlefield, 2014) 191-192, and Howard J. Wiarda, American Foreign Policy, Actors and Processes (New York: HarperCollins, 1996), 31.↑

2. For additional information on conducting a psychobiography see William Todd Schultz, Handbook of Psychobiography (Oxford UK: Oxford University Press, 2005). Also see Hudson, 58-61.↑

3. Michael W. Collier, Security Analysis, a Critical-Thinking Approach (Richmond, KY, Eastern Kentucky University Libraries Encompass, 2023), https://encompass.eku.edu/ekuopen/6/ or https://manifold.open.umn.edu/projects/security-analysis.↑

4. Modified from New Thinking Tools, “Four ways of seeing,” https://newthinking.tools/resources/your-creative-mission/four-ways-of-seeing/ (accessed December 19, 2020), attributed to University of Foreign Military and Cultural Studies, The Applied Critical Thinking Handbook, https://fas.org/irp/doddir/army/critthink.pdf (accessed December 19, 2020), 77.↑

5. U.S. Government, “A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis,” March 2009, https://www.stat.berkeley.edu/~aldous/157/Papers/Tradecraft%20Primer-apr09.pdf (accessed November 4, 2020), 27-29.↑

6. Brian Cole Miller, Quick Brainstorming Activities for Busy Managers (New York: American Management Association, 2012).↑

7. Ibid, 143.↑

8. See Richards J. Heuer Jr. and Randolph H. Pherson, Structured Analytic Techniques for Intelligence Analysis, 2^nd ed. (Los Angeles, CA: Sage, 2015) 209-214.↑

9. Stephen D. Brookfield, Teaching for Critical Thinking, Tools and Techniques to Help Students Question Their Assumptions (San Francisco, CA: Jossey-Bass, a Wiley Imprint, 2012). Amazon eBook edition.↑

10. Some references categorize assumptions differently from Brookfield and identify either value assumptions or descriptive assumptions. Value assumptions are a combination of Brookfield’s paradigmatic and prescriptive assumptions. Descriptive assumptions are similar to Brookfield’s causal assumptions. ↑

11. Brookfield.↑

12. Modified from Sarah Miller Beebe and Randolph H. Pherson, Cases in Intelligence Analysis, Structured Analytic Techniques in Action, 2^nd ed. (Los Angeles, CA: Sage/CQ Press, 2015) 55-57.↑

13. Barnet D. Feingold, “An Introduction to Critical Belief Analysis,” A Lecture Presented to the Williamsburg, VA, Philosophy Group, June 4, 2025.↑

14. Barnet D. Feingold and Michael W. Collier, Critical Belief Analysis for Security Studies (Richmond, KY, Eastern Kentucky University Libraries Encompass, 2024), Chapter 2, https://manifold.open.umn.edu/projects/critical-belief-analysis-for-security-studies.↑

15. Ibid, all Chapters.↑

16. Chapters 3 and 4 highlight the potential biases in just about all sources of information. Political discourse, government reporting, academic studies, and think tank analyses likely all have biases. Figure 3.5 and its following discussion highlights the potential political biases in all these sources. Media reporting is even more prone to biases as shown in Figures 3.3 and 3.6. Also, as highlighted in Chapter 3, data on government crime statistics should always be considered biased because there are no national standardized crime reporting requirements. The thinker would be well advised to assess the biases in all sources pertaining to their belief analyses. The Figure 3.7 Checklist for Assessing Information provides a template for thinkers to assess the quality of the information in their belief analyses.↑

17. In July 2024. Reuters published a summary of previous immigration studies. They concluded, based on the studies, that immigrants are not more likely to be involved in criminality than native-born U.S. citizens. The studies reviewed included those by academics (peer-reviewed) and think tanks. Reuters also cited studies that found the opposite of their conclusions, but were found flawed due to research methodological violations. See Ted Hesson and Mica Rosenberg, “Trump says migrants are fueling violent crime, Here is what research shows” Reuters, July 16, 2024 (https://www.reuters.com/world/us/trump-focuses-migrants-crime-here-is-what-research-shows-2024-04-11/ (accessed August 18, 2025).↑

18. For example, The CATO Institute, a centrist-leaning (libertarian) think tank, published a 2025 report on immigrant incarceration rates. They used incarceration rates as a proxy variable (substitute) for crime rates. They found native-born U.S. citizens were incarcerated more (i.e., committed more crimes) than immigrants. Their data on incarceration included the justice system holding alleged criminals for trial in jail and those convicted of crimes who were in jail or prison. Their reported per-capita results for 2023 (last year of available data) revealed the incarceration rates (per 100,000 U.S. residents) included:

    Native-Born U.S. Citizens 1,221

    Undocumented Immigrants 357

    Legal Immigrants and

    Naturalized U.S. Citizens 319

    The above does not include undocumented immigrants held in Immigration and Customs Enforcement detention for various immigration violations (usually misdemeanors)—if it did the undocumented immigrants’ total number was 613. Data back to 2010 showed native-born U.S. citizens have continually exceeded the per-capita incarceration rates of undocumented immigrants, even when detention data was included. See Michelangelo Landgrave and Alex Norasteh, “Illegal Immigrant Incarceration Rates, 2010-2023,” CATO Institute, April 24, 2025, https://www.cato.org/policy-analysis/illegal-immigrant-incarceration-rates-2010-2023 (accessed August 17, 2025).↑

19. A thinker must always assess the validity of the information, logic, and reasoning in their assessments, even more so in belief analysis. Chapter 3 provides techniques for assessing whether information is valid or invalid misinformation. Chapter 4 discusses how to identify cognitive biases, informal logic fallacies, and bad arguments that can all lead to degraded and biased thinking and assessments. Unfortunately, U.S. political discourse today is rampant with misinformation. This leads to situations where the U.S. populace is continually gaslit and often end up not knowing who or what to believe.↑