Personification of Wisdom (Koinē Greek: Σοφία, Sophía) at the Library of Celsus in Ephesus (second century) Photo credit: Traroth / CC BY-SA 3.0. Cropped.
Circular reasoning, also known as “circulus in probando” or “circular logic,” is a logical fallacy in which the conclusion of an argument is used as one of its premises. This fallacy, while seemingly convincing on the surface, ultimately lacks substance because the argument essentially supports itself without providing independent evidence. By understanding this fallacy, its implications, and examples, we can sharpen our reasoning skills and enhance critical thinking.
At its core, circular reasoning is a rhetorical sleight of hand where the argument’s validity depends on the very point it seeks to prove. Rather than constructing a linear chain of evidence leading to a conclusion, circular reasoning loops back on itself, making it a pragmatic defect rather than a formal logical fallacy.
Historical and Philosophical Context
Circular reasoning has been scrutinized for centuries, dating back to Ancient Greek philosophy. Aristotle’s exploration of circular reasoning in Metaphysics marked one of the earliest formal engagements with logical fallacies. The phrase τὸ ἐξ ἀρχῆς αἰτεῖν (“an assumption at the outset”) reflects Aristotle’s keen interest in the foundational principles of reasoning and argumentation. By identifying and analyzing this fallacy, Aristotle underscored the importance of constructing arguments that do not rely on their own conclusions to justify their premises. This early observation laid the groundwork for later logical discourse and became a cornerstone in the study of flawed reasoning.
The concept of circular reasoning evolved over time, influencing Latin scholars who coined the term petitio principii, or “begging the question,” to describe this logical misstep. During the medieval period, scholastic philosophers grappled with circular arguments in theological and logical discourses, further solidifying its role in logical analysis. Pyrrhonist philosopher Sextus Empiricus also highlighted circular reasoning as one of Agrippa’s Five Tropes, a set of philosophical challenges emphasizing skepticism. In this framework, circular reasoning is referred to as the “reciprocal trope,” where the verification of one idea depends entirely on another idea that itself requires verification.
The influence of circular reasoning extended beyond Ancient Greek philosophy, continuing to challenge thinkers across disciplines and eras. Over time, it was recognized not only as a logical fallacy but also as a rhetorical device, sometimes used intentionally to provoke thought or expose flawed assumptions. This dual role underscores the enduring fascination and complexity of circular arguments.
Philosopher David Hume also addressed circular reasoning through his “problem of induction.” Hume argued that inductive reasoning, which underpins scientific methods, assumes the principle of the uniformity of nature—that the future will resemble the past. However, justifying this principle itself relies on induction, making it circular. In modern philosophy and informal logic, circular reasoning often overlaps with concepts such as “begging the question.” While these terms are often used interchangeably, their subtle distinctions are worth exploring to fully understand the nuances of flawed reasoning.
Circular Reasoning and the Intelligence Cycle
The intelligence cycle is a structured, iterative process consisting of key phases: planning, collection, processing, analysis, and dissemination. This cycle enables intelligence agencies to transform raw information into actionable insights for decision-makers. However, the inherent complexity of the cycle also leaves it vulnerable to logical fallacies, with circular reasoning being particularly pernicious.
How Circular Reasoning Disrupts the Intelligence Cycle
Planning and Direction Phase: Biased Assumptions
Danger: Circular reasoning can taint this foundational phase when assumptions that drive the cycle are unjustified. For example, if policymakers assert that “Country X is a threat,” analysts may prioritize data collection to confirm this premise, inadvertently turning the hypothesis into a self-fulfilling prophecy.
Effect: These biases skew the cycle’s subsequent phases, leading to incomplete or misleading intelligence products.
Collection Phase: Skewed Data Gathering
Danger: If an unverified assumption shapes the collection of data, analysts may exclusively gather information supporting the predetermined conclusion while ignoring contradictory evidence.
Example: The belief that an adversary’s military movements are offensive (based on earlier assumptions) may lead to collecting intelligence only from regions of suspected conflict, ignoring peaceful zones or de-escalation efforts.
Effect: This selective focus compromises the breadth and objectivity of collected intelligence.
Processing Phase: Distorted Data Interpretation
Danger: Circular reasoning emerges when raw data is processed in ways that only reinforce existing assumptions. Analysts may interpret ambiguous signals in a manner consistent with the initial premise, rejecting alternative explanations.
Example: Routine troop exercises could be interpreted as preparations for an attack, solely because of an existing belief in the adversary’s aggressiveness.
Effect: Misinterpretation of data at this stage amplifies errors that ripple through the rest of the cycle.
Analysis Phase: Self-Reinforcing Conclusions
Danger: The analysis phase is particularly prone to circular reasoning, as conclusions are sometimes drawn based on premises that rely on those very conclusions. This creates an intellectual echo chamber.
Example: Analysts might conclude, “Country X is a threat because they are hostile,” without independent evidence supporting their hostility.
Effect: This undermines the credibility of intelligence assessments and risks producing flawed, action-guiding conclusions.
Dissemination Phase: Misleading End-Products
Danger: Circular reasoning perpetuates flawed narratives in final intelligence products delivered to policymakers or military commanders. These products may fail to challenge pre-existing beliefs or assumptions.
Effect: Policymakers may act on faulty intelligence, potentially escalating conflicts, misallocating resources, or missing opportunities for diplomacy.
Examples of Circular Reasoning in the Intelligence Cycle
Case Study: Weapons of Mass Destruction (WMDs) in Iraq (2003): Circular reasoning played a role in intelligence failures leading to the Iraq War. Preconceived notions of WMDs in Iraq influenced data collection, resulting in selective evidence being used to “prove” their existence. Assumptions about WMDs were repeatedly reaffirmed without independent verification, leading to flawed conclusions that justified military action.
Groupthink in Counterterrorism: In some counterterrorism cases, circular reasoning occurs when assumptions about a group’s intent lead to intelligence analyses that only consider data supporting the assumed narrative. This can blind agencies to alternative motives or actions by the group.
Strategies to Mitigate Circular Reasoning in the Intelligence Cycle
Critical Evaluation of Assumptions:
Challenge assumptions during the planning phase by questioning their validity and seeking independent evidence.
Incorporate “red teams” to provide alternative viewpoints and stress-test conclusions.
Diversifying Collection Efforts:
Ensure data collection is broad and unbiased, avoiding a narrow focus based on preconceived conclusions.
Use a variety of intelligence sources (e.g., HUMINT, SIGINT, OSINT) to cross-validate information.
Rigorous Processing Protocols:
Employ standardized methodologies for processing raw data, reducing subjective interpretations that feed circular reasoning.
Introduce automated systems for data analysis to counter human biases.
Structured Analysis Techniques:
Use methods like the Analysis of Competing Hypotheses (ACH) to systematically evaluate multiple explanations for the same data.
Regularly audit analytical processes to identify and correct logical fallacies.
Transparent Dissemination Practices:
Communicate uncertainties and alternative interpretations clearly in intelligence products.
Provide decision-makers with the context of assumptions and the limitations of the analysis.
Final Thoughts
Circular reasoning is an insidious fallacy that can easily pass unnoticed, perpetuating unsubstantiated claims and reinforcing pre-existing biases. Circular reasoning is a subtle yet significant threat within the intelligence cycle. By creating self-reinforcing feedback loops, it risks distorting analysis and compromising the quality of intelligence provided to decision-makers. However, with a commitment to critical thinking, rigorous methodologies, and a culture of accountability, intelligence agencies can break free from these intellectual traps.
Understanding the dangers of circular reasoning within the intelligence cycle is not just an exercise in logical rigor—it is essential for safeguarding the integrity of decision-making and protecting national security.
By breaking the cycle of circular reasoning, we open the door to genuine inquiry and understanding. After all, the essence of critical thinking lies in challenging assumptions, seeking evidence, and embracing the courage to question—even when it leads us out of comfortable intellectual loops.
