Circular causality is a concept rooted in systems theory and psychology that challenges traditional linear cause-and-effect thinking. It suggests that events and phenomena are interconnected in complex feedback loops, where causes and effects can influence each other in a continuous and cyclical manner.
Circular causality, also known as mutual causality or reciprocal causation, challenges the simplistic view of causality where one event directly leads to another in a linear fashion. Instead, it posits that causation is a dynamic and interconnected process where multiple factors interact with and influence each other. Key components of circular causality include:
Feedback Loops: Circular causality often involves feedback loops, where the effects of an action feed back into the system, influencing its future state.
Mutual Influence: It acknowledges that events or variables can influence each other simultaneously, creating a web of interdependencies.
Non-Linearity: Unlike linear causality, circular causality doesn’t adhere to a one-way cause-and-effect relationship. Instead, it embraces the idea that causation can be multifaceted and ongoing.
Real-World Applications
Circular causality finds applications in various fields and domains, including:
Psychology: It is used to understand the reciprocal relationship between behavior and environment, as seen in the nature-nurture debate.
Systems Thinking: Circular causality is a fundamental concept in systems thinking, helping analysts and decision-makers understand the dynamics of complex systems.
Economics: In economics, circular causality can explain the interactions between supply and demand, where changes in one influence the other.
Social Sciences: It is used to examine the complex interactions between social, economic, and cultural factors in societal phenomena.
Healthcare: In healthcare, it helps in understanding the interplay of various factors that contribute to the health and well-being of individuals and populations.
Advantages of Circular Causality
Circular causality offers several advantages:
Complexity Understanding: It provides a more nuanced and realistic understanding of complex systems, allowing for better decision-making.
Holistic Perspective: Circular causality encourages a holistic perspective, recognizing the interconnections and interdependencies in various phenomena.
Effective Problem-Solving: By considering multiple feedback loops and influences, circular causality can lead to more effective problem-solving strategies.
Behavioral Insights: In psychology and sociology, it helps in understanding the reciprocal relationship between behavior and the environment, leading to more effective interventions.
Disadvantages of Circular Causality
While circular causality has numerous advantages, it may also present challenges:
Complexity: Circular causality can be challenging to model and analyze due to its inherent complexity.
Causation Ambiguity: In some cases, it may be difficult to determine the direction of causation or identify the primary cause.
Predictive Difficulty: Predicting outcomes in systems with circular causality can be challenging, as small changes can lead to unpredictable consequences.
Data Analysis: Analyzing and quantifying circular causality in empirical data can be complex and require advanced statistical techniques.
Strategies for Understanding Circular Causality
To better understand circular causality, consider the following strategies:
Systems Thinking: Embrace systems thinking as a framework for understanding complex systems and their feedback loops.
Data Analysis Tools: Use advanced data analysis tools and techniques, such as structural equation modeling or system dynamics modeling, to explore circular causality.
Historical Analysis: Examine the historical evolution of systems and phenomena to identify recurring feedback loops and patterns.
Interdisciplinary Collaboration: Collaborate with experts from various fields to gain a comprehensive understanding of circular causality in complex systems.
Simulation and Modeling: Develop simulation models to explore the effects of different variables and feedback loops in complex systems.
Case Studies: Study real-world case examples where circular causality plays a significant role in understanding outcomes.
When Circular Causality Becomes a Concern
Circular causality may become a concern when:
Overemphasis on Complexity: Focusing excessively on circular causality may lead to analysis paralysis or an overly complex model that is difficult to manage.
Causation Ambiguity: In some situations, it may be challenging to establish clear causal relationships, leading to uncertainty.
Lack of Predictability: In complex systems, predicting outcomes with certainty can be difficult, which may be a concern in decision-making.
Ineffective Solutions: Overlooking key feedback loops or misinterpreting their impact may lead to ineffective solutions or interventions.
Conclusion
Circular causality is a valuable concept that challenges linear cause-and-effect thinking and offers a more nuanced understanding of complex systems and human behavior. By embracing circular causality, individuals, researchers, and decision-makers can better comprehend the interdependencies and feedback loops that shape various phenomena. It provides a powerful lens through which to examine complex systems, from ecological ecosystems to societal dynamics, and can lead to more effective problem-solving and decision-making in an interconnected and ever-changing world. Understanding circular causality is a crucial step towards unraveling the intricacies of the systems that surround us.
Aspect
Circular Causality
Definition
Circular causality, often associated with systems theory and family therapy, suggests that events or behaviors are mutually influenced by each other in a continuous feedback loop rather than in a linear cause-and-effect relationship. It proposes that multiple factors interact simultaneously to produce outcomes, and these outcomes, in turn, influence the factors that created them, creating a cycle of reciprocal causation.
Characteristics
– Feedback Loops: Involves interconnected feedback loops where causes and effects are constantly interacting and reinforcing each other.
– Nonlinear Dynamics: Recognizes that relationships and processes are nonlinear, with small changes in one part of the system leading to disproportionate effects elsewhere.
– Contextual Understanding: Emphasizes the importance of context and interdependence in understanding behaviors and outcomes.
Key Concepts
– Systems Thinking: Emphasizes holistic thinking and consideration of interconnections and interdependencies within systems.
– Mutual Influence: Proposes that events or behaviors are influenced by and influence each other in an ongoing circular process.
– Recursive Patterns: Recognizes repetitive patterns of behavior or interaction that perpetuate themselves through circular causation.
Applications
– Therapy and Counseling: Utilized in therapy and counseling to understand relationship dynamics and interpersonal conflicts within families or couples.
– Organizational Behavior: Applied in organizational behavior to analyze complex interactions between individuals, teams, and organizational structures.
– Social Sciences: Used in social sciences to study complex social phenomena and feedback mechanisms within societal systems.
Benefits
– Holistic Understanding: Facilitates a holistic understanding of complex phenomena by considering multiple factors and interdependencies.
– Intervention Insights: Provides insights for designing interventions that address root causes and feedback loops within systems.
– Predictive Capabilities: Enhances predictive capabilities by recognizing patterns of interaction and feedback that influence future outcomes.
Challenges
– Complexity: Dealing with the complexity of interconnected systems and multiple feedback loops can make analysis and intervention challenging.
– Causal Ambiguity: Identifying causal relationships in circular causality can be ambiguous and difficult to untangle.
– Dynamic Nature: Circular causality involves constantly evolving dynamics, making it challenging to capture static snapshots of causal relationships.
Convergent thinking occurs when the solution to a problem can be found by applying established rules and logical reasoning. Whereas divergent thinking is an unstructured problem-solving method where participants are encouraged to develop many innovative ideas or solutions to a given problem. Where convergent thinking might work for larger, mature organizations where divergent thinking is more suited for startups and innovative companies.
The concept of cognitive biases was introduced and popularized by the work of Amos Tversky and Daniel Kahneman in 1972. Biases are seen as systematic errors and flaws that make humans deviate from the standards of rationality, thus making us inept at making good decisions under uncertainty.
Second-order thinking is a means of assessing the implications of our decisions by considering future consequences. Second-order thinking is a mental model that considers all future possibilities. It encourages individuals to think outside of the box so that they can prepare for every and eventuality. It also discourages the tendency for individuals to default to the most obvious choice.
Lateral thinking is a business strategy that involves approaching a problem from a different direction. The strategy attempts to remove traditionally formulaic and routine approaches to problem-solving by advocating creative thinking, therefore finding unconventional ways to solve a known problem. This sort of non-linear approach to problem-solving, can at times, create a big impact.
Bounded rationality is a concept attributed to Herbert Simon, an economist and political scientist interested in decision-making and how we make decisions in the real world. In fact, he believed that rather than optimizing (which was the mainstream view in the past decades) humans follow what he called satisficing.
The Dunning-Kruger effect describes a cognitive bias where people with low ability in a task overestimate their ability to perform that task well. Consumers or businesses that do not possess the requisite knowledge make bad decisions. What’s more, knowledge gaps prevent the person or business from seeing their mistakes.
Occam’s Razor states that one should not increase (beyond reason) the number of entities required to explain anything. All things being equal, the simplest solution is often the best one. The principle is attributed to 14th-century English theologian William of Ockham.
The Lindy Effect is a theory about the ageing of non-perishable things, like technology or ideas. Popularized by author Nicholas Nassim Taleb, the Lindy Effect states that non-perishable things like technology age – linearly – in reverse. Therefore, the older an idea or a technology, the same will be its life expectancy.
Antifragility was first coined as a term by author, and options trader Nassim Nicholas Taleb. Antifragility is a characteristic of systems that thrive as a result of stressors, volatility, and randomness. Therefore, Antifragile is the opposite of fragile. Where a fragile thing breaks up to volatility; a robust thing resists volatility. An antifragile thing gets stronger from volatility (provided the level of stressors and randomness doesn’t pass a certain threshold).
Systems thinking is a holistic means of investigating the factors and interactions that could contribute to a potential outcome. It is about thinking non-linearly, and understanding the second-order consequences of actions and input into the system.
Vertical thinking, on the other hand, is a problem-solving approach that favors a selective, analytical, structured, and sequential mindset. The focus of vertical thinking is to arrive at a reasoned, defined solution.
Maslow’s Hammer, otherwise known as the law of the instrument or the Einstellung effect, is a cognitive bias causing an over-reliance on a familiar tool. This can be expressed as the tendency to overuse a known tool (perhaps a hammer) to solve issues that might require a different tool. This problem is persistent in the business world where perhaps known tools or frameworks might be used in the wrong context (like business plans used as planning tools instead of only investors’ pitches).
The Peter Principle was first described by Canadian sociologist Lawrence J. Peter in his 1969 book The Peter Principle. The Peter Principle states that people are continually promoted within an organization until they reach their level of incompetence.
The straw man fallacy describes an argument that misrepresents an opponent’s stance to make rebuttal more convenient. The straw man fallacy is a type of informal logical fallacy, defined as a flaw in the structure of an argument that renders it invalid.
The Streisand Effect is a paradoxical phenomenon where the act of suppressing information to reduce visibility causes it to become more visible. In 2003, Streisand attempted to suppress aerial photographs of her Californian home by suing photographer Kenneth Adelman for an invasion of privacy. Adelman, who Streisand assumed was paparazzi, was instead taking photographs to document and study coastal erosion. In her quest for more privacy, Streisand’s efforts had the opposite effect.
As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.
The recognition heuristic is a psychological model of judgment and decision making. It is part of a suite of simple and economical heuristics proposed by psychologists Daniel Goldstein and Gerd Gigerenzer. The recognition heuristic argues that inferences are made about an object based on whether it is recognized or not.
The representativeness heuristic was first described by psychologists Daniel Kahneman and Amos Tversky. The representativeness heuristic judges the probability of an event according to the degree to which that event resembles a broader class. When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.
The take-the-best heuristic is a decision-making shortcut that helps an individual choose between several alternatives. The take-the-best (TTB) heuristic decides between two or more alternatives based on a single good attribute, otherwise known as a cue. In the process, less desirable attributes are ignored.
The bundling bias is a cognitive bias in e-commerce where a consumer tends not to use all of the products bought as a group, or bundle. Bundling occurs when individual products or services are sold together as a bundle. Common examples are tickets and experiences. The bundling bias dictates that consumers are less likely to use each item in the bundle. This means that the value of the bundle and indeed the value of each item in the bundle is decreased.
The Barnum Effect is a cognitive bias where individuals believe that generic information – which applies to most people – is specifically tailored for themselves.
First-principles thinking – sometimes called reasoning from first principles – is used to reverse-engineer complex problems and encourage creativity. It involves breaking down problems into basic elements and reassembling them from the ground up. Elon Musk is among the strongest proponents of this way of thinking.
The ladder of inference is a conscious or subconscious thinking process where an individual moves from a fact to a decision or action. The ladder of inference was created by academic Chris Argyris to illustrate how people form and then use mental models to make decisions.
Goodhart’s Law is named after British monetary policy theorist and economist Charles Goodhart. Speaking at a conference in Sydney in 1975, Goodhart said that “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Goodhart’s Law states that when a measure becomes a target, it ceases to be a good measure.
The Six Thinking Hats model was created by psychologist Edward de Bono in 1986, who noted that personality type was a key driver of how people approached problem-solving. For example, optimists view situations differently from pessimists. Analytical individuals may generate ideas that a more emotional person would not, and vice versa.
The Mandela effect is a phenomenon where a large group of people remembers an event differently from how it occurred. The Mandela effect was first described in relation to Fiona Broome, who believed that former South African President Nelson Mandela died in prison during the 1980s. While Mandela was released from prison in 1990 and died 23 years later, Broome remembered news coverage of his death in prison and even a speech from his widow. Of course, neither event occurred in reality. But Broome was later to discover that she was not the only one with the same recollection of events.
The bandwagon effect tells us that the more a belief or idea has been adopted by more people within a group, the more the individual adoption of that idea might increase within the same group. This is the psychological effect that leads to herd mentality. What in marketing can be associated with social proof.
Moore’s law states that the number of transistors on a microchip doubles approximately every two years. This observation was made by Intel co-founder Gordon Moore in 1965 and it become a guiding principle for the semiconductor industry and has had far-reaching implications for technology as a whole.
Disruptive innovation as a term was first described by Clayton M. Christensen, an American academic and business consultant whom The Economist called “the most influential management thinker of his time.” Disruptive innovation describes the process by which a product or service takes hold at the bottom of a market and eventually displaces established competitors, products, firms, or alliances.
Value migration was first described by author Adrian Slywotzky in his 1996 book Value Migration – How to Think Several Moves Ahead of the Competition. Value migration is the transferal of value-creating forces from outdated business models to something better able to satisfy consumer demands.
The bye-now effect describes the tendency for consumers to think of the word “buy” when they read the word “bye”. In a study that tracked diners at a name-your-own-price restaurant, each diner was asked to read one of two phrases before ordering their meal. The first phrase, “so long”, resulted in diners paying an average of $32 per meal. But when diners recited the phrase “bye bye” before ordering, the average price per meal rose to $45.
Groupthink occurs when well-intentioned individuals make non-optimal or irrational decisions based on a belief that dissent is impossible or on a motivation to conform. Groupthink occurs when members of a group reach a consensus without critical reasoning or evaluation of the alternatives and their consequences.
A stereotype is a fixed and over-generalized belief about a particular group or class of people. These beliefs are based on the false assumption that certain characteristics are common to every individual residing in that group. Many stereotypes have a long and sometimes controversial history and are a direct consequence of various political, social, or economic events. Stereotyping is the process of making assumptions about a person or group of people based on various attributes, including gender, race, religion, or physical traits.
Murphy’s Law states that if anything can go wrong, it will go wrong. Murphy’s Law was named after aerospace engineer Edward A. Murphy. During his time working at Edwards Air Force Base in 1949, Murphy cursed a technician who had improperly wired an electrical component and said, “If there is any way to do it wrong, he’ll find it.”
The law of unintended consequences was first mentioned by British philosopher John Locke when writing to parliament about the unintended effects of interest rate rises. However, it was popularized in 1936 by American sociologist Robert K. Merton who looked at unexpected, unanticipated, and unintended consequences and their impact on society.
Fundamental attribution error is a bias people display when judging the behavior of others. The tendency is to over-emphasize personal characteristics and under-emphasize environmental and situational factors.
Outcome bias describes a tendency to evaluate a decision based on its outcome and not on the process by which the decision was reached. In other words, the quality of a decision is only determined once the outcome is known. Outcome bias occurs when a decision is based on the outcome of previous events without regard for how those events developed.
Hindsight bias is the tendency for people to perceive past events as more predictable than they actually were. The result of a presidential election, for example, seems more obvious when the winner is announced. The same can also be said for the avid sports fan who predicted the correct outcome of a match regardless of whether their team won or lost. Hindsight bias, therefore, is the tendency for an individual to convince themselves that they accurately predicted an event before it happened.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
