This reinforcing feedback loop would be characterized as a virtuous feedback loop since the growth around the feedback loop is helping achieve the company's goals. (Location 362)
The negative effects of virtuous turned vicious loops is an extremely important lesson to keep in mind. (Location 378)
First, sensory inputs are fed into System 1. System 1 takes the inputs and makes initial sense out of them. (Location 478)
System 2 is the primary component of what we consider consciousness. (Location 479)
The grandmaster has a different way to think about the chess game than his opponents. (Location 486)
At the core, we cover the emergent effects from the dynamics of feedback and delays (Chapter 3) (Location 594)
Systems science encompasses all of game theory, dynamics, cybernetics, information theory, (Location 605)
complex adaptive systems, chaos, and many more theories dealing with interactions. (Location 606)
Complexity can be categorized into two types: combinatorial and dynamic. (Location 608)
In the case of dynamics, changes through time create the complexity, like the anthill we discussed in the first chapter. In this case, engineering techniques may struggle. Dynamic complexity is the topic addressed by systems thinking. (Location 612)
One simple model for the process of intelligence in the creation and use of tools is illustrated in the upper row of boxes in Figure 10. (Location 644)
While evolution is optimizing overall system (animal) fitness, there are metrics we might use to get a handle on a subsystem performance. (Location 664)
One of the centerpieces of efficient intelligence is mental heuristics. Sometimes referred to as a rule of thumb, a heuristic is a mental shortcut. (Location 673)
It shouldn't surprise us then that the human mind has been evolving to perform well in that role. (Location 679)
The human mind will normally assume trends are linear. However, in most complex systems, trends tend to be nonlinear. (Location 726)
System 2 takes the output of System 1 and produces the thoughts we refer to as consciousness. (Location 733)
System 2 burns a lot of fuel (glucose from the bloodstream), and the tendency to accept the recommendations of System 1 reduces its overall fuel usage. (Location 734)
Taleb defines the narrative fallacy as taking a sequence of facts and binding them together in a story. The created story invariably includes arrows of causal relationships between the facts. (Location 740)
humans to the mental conclusion that a rare event was predictable since the precedent events are all clearly linked together in the mind. (Location 742)
Kahneman calls this a conjunction fallacy. (Location 768)
Modeling—a process to develop a representation of reality to understand, communicate, or predict. (Location 801)
but if the analyst understands the model's limitations, the model can be quite valuable. (Location 808)
She infers that advertisements that focus on loss prevention would be more impactful than those that focus on gains. (Location 815)
Deductive reasoning, the bedrock of the scientific method, begins with a new theory which leads to a prediction (a hypothesis). The real world is tested and observed to determine if the theory is supported. One can think of deduction as being top down or theory driven (or general to specific). Induction, on the other hand, begins with observations from the real world. Based on these observations, patterns are recognized and new theories proposed (specific to general). The different methods have important implications on model construction. (Location 830)
the test for support of the hypothesis is a critical feedback mechanism. Induction, on the other hand, does not have an implicit feedback mechanism. (Location 835)
This general process is called calibration, and it is inherently inductive. (Location 847)
A precarious tendency of model developers is to add more elements to the model in an effort to create a better match to real-world behavior. (Location 848)
Observations that support the hypothesis only provide support. The hypothesis cannot be proven to be true. On the other hand, a single data point can disprove the hypothesis. We will revisit this again shortly in the section on Black Swans. (Location 857)
understanding the limitations of this use will be paramount. A critical point that will be made again and again in this book is that predictions are quite difficult in complex systems. (Location 859)
However, this underpinning of tool usage can be misguided when applied to complex systems. (Location 875)
there may be no simple cause to an observed effect. We must caution ourselves from summarily accepting simple explanations since our System 1/System 2 complex will be at work constructing possible causes. (Location 876)
Covariation—Correlation between cause and effect as illustrated in the table below. (Location 893)
the Bloomberg News flashed the headline "U.S. Treasuries Rise; Hussein Capture May Not Curb Terrorism." (Location 909)
The journalists felt the need to link the events into a causal relationship. (Location 912)
As illustrated in Figure 14, we first consider the markets as powerful information-gathering devices, as illustrated in the market reaction to the Challenger disaster. (Location 923)
Journalists, eager to write newspaper-selling stories, watch the stock market, observe large changes in a particular stock, and build probable simple causes for what they observe. (Location 926)
A theme we will revisit often is that prediction is very difficult in complex systems. (Location 935)
It is natural for humans to look for ways to predict the future. (Location 937)
However, at its core, the Black Swan is a modeling problem. (Location 995)
The all swans are white hypothesis is based on inductive reasoning. (Location 996)
The turkey has formed the hypothesis that humans are benevolent towards turkeys. (Location 1001)
If the turkey is statistically inclined, he may be computing p values (or confidence levels), which are increasingly meeting statistically significant levels. (Location 1002)
Emergence: A phenomenon where an interaction among objects at one level generates new types of objects at another level. The emergent characteristic requires a new descriptive category at that next level. (Location 1010)
System scientists might debate if emergence is subjective or objective. (Location 1016)
we recall George Box's refrain that all simulations are wrong but some are useful. (Location 1022)
complex systems may contain points of high leverage. (Location 1025)
Using system models to identify high leverage points is the primary method of finding unanticipated solutions! (Location 1027)
Systems models afford the user the chance to characterize the basic modes of the system. (Location 1030)
Models are very valuable to communicate and teach. (Location 1033)
In the area of qualitative dynamics, we seek to understand how dynamic factors, feedback and delay, may affect behavior. (Location 1098)
qualitative is used in the sense that qualitative research is differentiated from quantitative methods. (Location 1099)
For qualitative dynamics, the key idea is that structure impacts behavior through time as depicted in Figure 15. (Location 1108)
Reinforcing feedback, also called positive feedback, occurs when a disturbance will be amplified as it travels around the loop. (Location 1119)
process makes the balance grow exponentially. This is the emergent property in this system. (Location 1131)
As time progresses, a fraction of the system variable is removed from the system variable. (Location 1136)
Delay is another crucial component in the structure of dynamic systems. (Location 1145)
In this balancing feedback loop system, the value of the system variable is compared to a target level for this variable, labeled here as a goal. (Location 1148)
A crucial parameter in this system is the carrying capacity. In biological systems, the carrying capacity might be the amount of food available for a species in an ecosystem. (Location 1173)
In business, we may see this shape in a product sales chart. (Location 1184)
If delays are substantial, the system may oscillate as it approaches the carrying capacity limits. (Location 1186)
For the linked feedback loops of Figure 20, the slowing of growth resulting from the enhanced impact of the reinforcing loop may be a source of concern to the business that is depending on continued growth. (Location 1195)
In the early stage of exponential growth, data will be scarce. This scarcity makes the data much more susceptible to random fluctuations. (Location 1203)
Finally, as sales slowly taper, there is a tendency for overestimation. (Location 1215)
The solar energy market demand had slowed as the balancing feedback loop driven by competition with other energy sources kicked in. (Location 1217)
However, the balancing loop is effectively dormant. It's only later in time that the balancing loop reveals itself in the data set. This leads to errors in misidentifying the system and errors in predicting the future state of the system. (Location 1220)
One school of thought is that system boundaries are natural. For example, our solar system might represent a complete system with the vast distance to the next solar system being the factor that defines the system (philosophically this could be considered an ontological view). (Location 1225)
Systems reside in a nested structure; that is, each component in a system is a system in itself. (Location 1231)
to view variables that are usually considered out of our control (that is, exogenous) as parts of the system (or endogenous). In fact, this is one of the important messages from the key idea graphic (Figure 15). (Location 1238)
Senge (1990) introduced the idea of system archetypes as being connections of feedback loops and delays that are frequently seen in complex human social systems. (Location 1242)
the stages of the supply chain in a temporary equilibrium. There is a sudden increase in consumer demand in one particular beer due to, as we find out later, a music video that features that beer. (Location 1272)
As demand quickly outstrips stock, players order more inventory. Due to the delay in the system, the stock cannot be resupplied fast enough. Internally generated pressure provokes the player to order even more product. (Location 1275)
All of the decisions seem rational at the time. (Location 1283)
whether it be not delivering beer when their customer's need it, or delivering too much beer as the inventory piles (Location 1284)
The system structure drives the effect of shortages followed by stockpiles. (Location 1287)
When the analyst starts to graph out this feedback loop, it becomes apparent that the loop itself is heterogeneous—part of the loop is physical inventory (beer) and part of the loop is information (orders). (Location 1290)
whereas the VHS engineers gave up some recording quality to extend the maximum recording time. (Location 1303)
Sales of VCR units is the key state variable. As sales increase, new entrants, makers of consumer electronics, become interested in the potential for profits. (Location 1308)
The structure of this market competition is an example of Senge's Success to the Successful archetype ( Figure 25). (Location 1334)
The Apple Macintosh computers were based on non-x86 processors and were not able to run the same code. (Location 1348)
If the x86 processor development suddenly fell behind schedule, the tendency would be to divert resources away from the second microchip development to shore up the development of the x86 processor. (Location 1355)
Finally, it is worth noting that in the real world there are always more feedback loops at work. (Location 1404)
Identify Feedback Loops and Delays—In the beer game, participants must recognize that there is a certain amount of time to get an order to the wholesaler, and there's also a delay in the wholesaler to the brewery. (Location 1430)
for Building Blocks—Look at your diagram of feedback loops and delays to identify potential building blocks, either linked feedback loops or archetypes. (Location 1433)
for Leverage Points and Potential Unintended Consequences of Interventions—When a model produces dynamic behavior like the real system, start to examine the model for insights. Are there key leverage points in your model? (Location 1442)
“All models are wrong, but some are useful.” (Location 1449)
Each agent acts based on their own unique situation using a set of guiding principles called rules in ABM. (Location 1804)
There are various types of processes that rely on the aggregation of interactions: (Location 1835)
We will follow in the footsteps of the Nobel Prize winning economist and sociologist Thomas Schelling in this example. This example will also be used to introduce agent-based simulation using NetLogo. (Location 1859)
When the go button is pressed, the simulation will assess each agent, and if any agent is unhappy, will attempt to move them. (Location 1885)
The stock market reaction to the Challenger Space Shuttle explosion was discussed in Chapter 1. The market seemed to figure out what happened within a few hours, while the experts were publicly saying that they had no ideas. (Location 1956)
The feedback loop of price, market information, and participants, which enables the market to assimilate the collected information, can also create issues. (Location 2035)
Mark Granovetter (1978) wrote about a simple version of this class of model where participants have two choices and their choice is dependent on the choices of others in the group. (Location 2138)
Lorenz curve is often used in economics and expresses the cumulative distribution function of the sugar stores. (Location 2228)
The inequality is instead an emergent property of the interactions between agents and the environment. Since this effect comes about from aggregation of all the interactions, there is no simple cause. (Location 2243)
New Emergent Effects: Large oscillations in populations and potential for endogenous extinction events. (Location 2254)
Prices did not reach equilibrium as predicted by classical economics. Instead, prices tended to move dynamically around attractor values resembling the action of chaotic systems. (Location 2258)
First, Page proposes the Diversity Prediction Theorem, which asserts that diversity and accuracy contribute equally to group performance. (Location 2271)
In his excellent book Turtles, Termites, and Traffic Jams (1997), Mitchel Resnick outlines a list of five heuristics associated with the aggregation of interactions: (Location 2302)
Some people view randomness as destructive, but in some cases it actually helps make systems more orderly. (Location 2318)
People often focus on the behaviors of individual agents and overlook the environment that surrounds the agents. In the foraging ants example, the ground plays an important role in that it holds the pheromone that the ants have dropped. (Location 2328)
This type of effect is called second order emergence and is illustrated in Figure 48 with a new arrow that feeds back from the emergent effect to the agents. (Location 2335)
The planner's responsibility was fairly straight-forward: schedule the fab capacity. (Location 2374)
These are decentralized and self-organized processes. (Location 2395)
It was the dominance of the choice to rat out the other that drove the group to a deficient outcome. The individuals were rational, but the group was irrational! (Location 2509)
Winning a thousand dollar lottery would be a big deal to a student tired of macaroni and cheese for dinner. But, a thousand dollar lottery wouldn't even make the radar of the CEO of a large corporation[17] (Location 2534)
Cooperate to indicate that the prisoner is attempting to work with the other prisoner to get the best result. (Location 2540)
The first order of business is to check for a dominant strategy. (Location 2573)
What if he knew the column player was not going to swerve? Then, clearly, he would swerve. (Location 2574)
This irrevocable commitment will affect the outcome since the other player must now swerve. (Location 2578)
First, this situation represents a class of game called zero-sum. That is, the sum of the payoffs in each square is zero[21] (Location 2620)
results from their decision depends on how others will act. In business, an example is a team looking at lowering the price of a product to increase volume and not considering that the competition could meet, or even further lower, their price and nullify any market share gains. (Location 2721)
Fairness is another extremely important aspect in strategic situations. (Location 2764)
In fact, unless the offer is approximately a third of the total, the second player usually rejects the offer. (Location 2768)
The discussion on antifragility goes on to study some important characteristics of evolutionary systems. (Location 3046)
Beinhocker explicitly makes that assertion and equates a company's business plans to the DNA structure of biological systems (Location 3093)
Sponsor trial and error in your organization—viva la entrepreneur. (Location 3318)
With the idea of convexity, one expects some failures, but a small number of big successes. (Location 3321)
Things That Have Been Around for a While, Tend to Be Around for a While Longer—In (Location 3322)
Taleb suggests, as a very general rule of thumb, one could expect a technology to persist going forward for at least the amount of time it has been in existence. (Location 3325)
Instead, the avalanche was a result of the sand pile being in a super-critical state. That one grain of sand was just the final straw, a catalyst, for the avalanche. (Location 3356)
if your business ecosystem seems stable, don't be lulled into a potentially false sense of security. (Location 3360)
Models used in business often do very well in periods of stability, but fail miserably in times of sudden changes. (Location 3362)
Constructing an ecosystem is hard, but not impossible. SAP has had success. But be prepared to spend substantial resources to nurture it. (Location 3410)
Seek out low-cost options in your business and look for convex payouts. o  Embrace trial and error processes, and foster entrepreneurship. o  Embrace randomness, and be aware that attempts to mask or limit its effects may seem to work in the short run but could have big consequences in the future. o  Value diversity; it is important in evolutionary processes. (Location 3424)
In our systems thinking viewpoint, an organization, a collection of individuals, has an emergent behavior unique from those of the individuals. (Location 3854)