Systems feedback, characterized by looping and influential dynamics, includes positive feedback amplifying deviations and negative feedback maintaining stability. It benefits systems by enabling adaptation and control but poses challenges of instability and complexity. Examples include thermostat control for room temperature and stock market behavior influenced by buying and selling.
Systems feedback is a fundamental concept in the field of systems theory and engineering. It refers to the process of obtaining information about the output of a system and using that information to make adjustments or modifications to the system’s input. Feedback mechanisms play a critical role in maintaining stability, regulating behavior, and achieving desired outcomes in complex systems.
Key Components of Systems Feedback:
Input and Output: Feedback involves monitoring the output (or results) of a system and comparing it to the desired or expected output.
Error Detection: Feedback mechanisms identify discrepancies or errors between the actual output and the desired output.
Adjustment: Based on error detection, feedback mechanisms trigger adjustments to the system’s input or control parameters to reduce the detected errors.
Why Systems Feedback Matters
Understanding the significance of systems feedback is crucial for professionals, engineers, and decision-makers in various fields, as it influences the efficiency, stability, and adaptability of complex systems.
The Impact of Systems Feedback:
System Stability: Feedback loops are essential for maintaining the stability of complex systems by continuously fine-tuning their behavior.
Adaptability: Feedback enables systems to adapt to changing conditions and achieve optimal performance.
Benefits of Systems Feedback:
Error Correction: Feedback mechanisms identify and correct errors, reducing the likelihood of system failures or suboptimal outcomes.
Optimization: By adjusting system parameters based on feedback, organizations can optimize processes and resources.
Challenges in Implementing Systems Feedback:
Complexity: In highly complex systems, managing and interpreting feedback data can be challenging.
Delayed Effects: Some feedback mechanisms may have delayed effects, making it difficult to anticipate and respond to changes.
Characteristics of Systems Feedback
Looping: The core characteristic of systems feedback is the concept of looping. Feedback creates a continuous cycle of interactions where the outcomes of a system’s actions become inputs for subsequent actions. This ongoing loop of influence and response is a defining feature of feedback. It highlights the interconnectedness of elements within a system and the potential for circular causality.
Influence: Feedback mechanisms have a profound influence on a system’s behavior. They can cause the system to adjust, adapt, or change in response to the feedback it receives. Whether reinforcing deviations or counteracting them, feedback plays a pivotal role in shaping the dynamics of a system. It can lead to both short-term adjustments and long-term systemic changes.
Types of Systems Feedback
Positive Feedback: Positive feedback amplifies deviations or changes from the system’s desired or set point. It intensifies the initial deviation, potentially leading to rapid and significant system changes. An example of positive feedback is the process of labor during childbirth, where contractions intensify as labor progresses. While positive feedback can lead to rapid change, it can also result in instability if left unchecked.
Negative Feedback: Negative feedback, in contrast, counteracts deviations or changes from the system’s desired state. It acts as a stabilizing force, working to bring the system back to its set point or equilibrium. The human body uses negative feedback, such as thermoregulation, to maintain a relatively constant body temperature. Negative feedback is essential for maintaining stability and preventing extreme deviations.
Benefits of Systems Feedback
Adaptation: Feedback mechanisms, both positive and negative, enable systems to adapt to changing conditions. This adaptability is crucial for the survival and efficient operation of various systems. For example, ecosystems can adapt to environmental changes through feedback mechanisms that regulate population dynamics and resource utilization.
Control: Feedback provides control mechanisms within systems. It allows systems to achieve and maintain specific goals, parameters, or states. By using feedback to monitor and regulate system behavior, organizations and processes can operate effectively and efficiently. For instance, feedback control systems in engineering help maintain the desired performance of machines and processes.
Challenges of Systems Feedback
Instability: One of the challenges associated with feedback is the potential for instability, particularly in systems with positive feedback loops. If not properly managed or controlled, positive feedback can lead to runaway processes and instability. Examples include financial market bubbles driven by positive feedback loops of buying and price increases.
Complexity: Managing feedback in complex systems can be challenging due to the intricate interplay of variables and the potential for unexpected or unintended consequences. Understanding and predicting the outcomes of feedback loops can be complex, requiring sophisticated modeling and analysis. Complex systems, such as ecological food webs or global financial markets, often exhibit emergent behavior resulting from feedback interactions.
Examples of Systems Feedback
Thermostat Control: A classic example of negative feedback is the operation of a thermostat in a room. When the temperature deviates from the desired set point (e.g., it gets too cold), the thermostat activates the heating system. Once the temperature returns to the set point, the thermostat turns off the heat, maintaining a consistent temperature. Negative feedback stabilizes the room’s temperature and prevents extreme fluctuations.
Stock Market Behavior: Financial markets exhibit feedback effects. For instance, positive news can lead to increased buying activity, creating a positive feedback loop that drives up stock prices. Conversely, negative news can trigger selling, creating a negative feedback loop that stabilizes or reduces prices. Understanding these feedback mechanisms is essential for investors and policymakers to navigate the complexities of financial markets.
Case Studies
Biological Examples:
Blood Glucose Regulation: In the human body, blood glucose levels are regulated through negative feedback. When blood sugar levels rise after eating, the pancreas releases insulin, which facilitates the uptake of glucose by cells, bringing blood sugar back to a normal range.
Hormone Regulation: The endocrine system relies on feedback loops to control hormone levels. For instance, the hypothalamus-pituitary-adrenal (HPA) axis regulates stress hormones. When stress levels rise, the system releases cortisol. Once stress subsides, cortisol production decreases.
Ecological Examples:
Ecosystem Balance: Ecological systems use feedback loops to maintain balance. Predators and prey populations are interrelated through feedback. If prey populations increase, it leads to more food for predators, which can then increase in number, causing prey populations to decline, and the cycle continues.
Carbon Cycle: The Earth’s carbon cycle involves feedback mechanisms. As carbon dioxide (CO2) levels rise in the atmosphere due to human activities, such as burning fossil fuels, it leads to global warming. In response, natural processes like increased plant growth and enhanced ocean absorption act as negative feedback, partially offsetting CO2 increases.
Engineering and Technology Examples:
Aircraft Control: Modern aircraft use feedback systems to maintain stability during flight. Sensors monitor factors like altitude and speed, and control surfaces (elevators, ailerons) adjust to keep the aircraft within desired parameters.
Network Congestion Control: In computer networks, feedback mechanisms control data flow. When congestion is detected, feedback signals are sent to reduce the rate of data transmission, preventing network overload.
Social and Economic Examples:
Economic Market Corrections: Financial markets experience feedback effects. For example, when stock prices rise significantly (positive feedback), it can lead to speculative bubbles. Eventually, market corrections occur as prices adjust to more reasonable levels.
Traffic Flow Control: Traffic management systems in cities employ feedback to control traffic flow. Traffic lights adjust their timings based on real-time traffic conditions, optimizing traffic movement.
Key Highlights
Continuous Loops: Systems feedback involves continuous loops of information or effects, where outputs influence inputs, creating dynamic and iterative processes.
Influence on Behavior: Feedback has a significant influence on a system’s behavior, allowing it to adapt, self-regulate, and respond to changes.
Two Types: There are two primary types of systems feedback:
Positive Feedback: Amplifies deviations from the desired state, potentially leading to rapid and significant system changes.
Negative Feedback: Counteracts deviations, maintaining system stability and equilibrium.
Benefits:
Adaptation: Feedback mechanisms enable systems to adapt to changing conditions, ensuring their survival and optimal performance.
Control: Feedback provides control mechanisms that allow systems to achieve and maintain specific goals, parameters, or states.
Challenges:
Instability: Positive feedback can lead to instability if not controlled, as it amplifies deviations.
Complexity: Managing feedback in complex systems can be challenging due to the intricate interplay of variables and potential unintended consequences.
Real-World Examples: Systems feedback is observed in various domains, including biology (blood glucose regulation), ecology (ecosystem balance), technology (aircraft control), economics (market corrections), and traffic management.
Balance and Regulation: Feedback mechanisms are fundamental for maintaining balance, stability, and regulation in both natural and human-made systems.
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.
