Introduction to Engineering Systems, ESD.00. System Dynamics. Lecture 3. Dr. Afreen Siddiqi


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1 Introduction to Engineering Systems, ESD.00 System Dynamics Lecture 3 Dr. Afreen Siddiqi
2 From Last Time: Systems Thinking we can t do just one thing things are interconnected and our actions have numerous effects that we often do not anticipate or realize. Goals Decisions Many times our policies and efforts aimed towards some objective fail to produce the desired outcomes, rather we often make matters worse Systems Thinking involves holistic consideration of our actions Environment Ref: Figure 14, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
3 Dynamic Complexity Dynamic (changing over time) Governed by feedback (actions feedback on themselves) es) Nonlinear (effect is rarely proportional to cause, and what happens locally often doesn t apply in distant regions) History dependent (taking one road often precludes taking others and determines your destination, you can t unscramble an egg) Adaptive (the capabilities and decision rules of agents in complex systems change over time) Counterintuitive (cause and effect are distant in time and space) Policy resistant (many seemingly obvious solutions to problems fail or actually worsen the situation) Ch i d b d ff (h l i f diff f h Characterized by trade offs (the long run is often different from the short run response, due to time delays. High leverage policies often cause worse before better behavior while low leverage policies often generate transitory improvement before the problem grows worse.
4 Modes of Behavior Exponential Growth Goal Seeking Sshaped Growth Time Time Time Oscillation Growth with Overshoot Overshoot and Collapse Time Time Time Ref: Figure 41, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
5 Exponential Growth Arises from positive (self reinforcing) feedback. In pure exponential growth the state of the system doubles in a fixed period of time. Same amount of time to grow from 1 to 2, and from 1 billion to 2 billion! Self reinforcing feedback can be a declining loop as well (e.g. stock prices) Common example: compound interest, population growth Ref: Figure 42, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000 Net increase rate Time R State of the system State of the system
6 Exponential Growth: Examples CPU Transistor Counts & Moore s Law Transistor count 2,000,000,000 1,000,000, ,000,000 10,000, , ,000 Curve shows Moore s Law ; Transistor count doubling every two years QuadCore Itanium Tukwila DualCore Itanium 2 POWER6 GT200 G80 RV770 Itanium 2 with 9 MB cache K10 Core 2 Quad Core 2 Duo Itanium 2 Cell P4 K7 K6III K6 PII PIII K5 Pentium K8 Barton Atom ,000 2, Ref: wikipedia Date of Introduction
7 Some Positive Feedbacks underlying Moore s Law Design tool capability R5 Design bootstrap Computer performance R2 Chip performance relative to competitors Differentiation advantage Applications for chips Transistors per chips Design and R4 fabrication experience Learning by doing Design and fabrication capability R1 New uses, New needs Demand for Intel chips R3 Price premium Price premium for performance R&D budget, Investment in manufactutring capability Revenue Ref: Exhibit 47 7, J. Sterman, Instructor s s Manual, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill,
8 Goal Seeking Negative loops seek balance, and equilibrium, and try to bring the system to a desired state (goal). Positive loops reinforce change, while negative e loops counteract change or disturbances. Negative loops have a process to compare desired state to current state and take corrective action. Pure exponential decay is characterized by its half life the time it takes for half the remaining gap to be eliminated. State of the system State of the system B Corrective action Goal Time  Discrepancy Goal (desired state of system) Ref: Figure 44, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
9 Oscillation This is the third fundamental mode of behavior. It is caused by goal seeking behavior, but results from constant over shoots and under shoots Time State of the system Goal The over shoots and under shoots result due to time delays the corrective action continues to execute even when system reaches desired state giving rise to the oscillations. Action delays Delay State of the system B Corrective action Delay Discrepancy Delay Measurement, reporting and perception delays  Goal (desired state of system) Administrative and decision making delays Ref: Figure 46, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
10 Interpreting Behavior Connection between structure and behavior helps in generating hypotheses If exponential growth is observed > some reinforcing feedback loop is dominant over the time horizon of behavior If oscillations are observed, think of time delays and goal seeking behavior. Past data shows historical behavior, the future maybe different. Dormant underlying structures may emerge in the future and change the mode It is useful to think what future modes can be, how to plan and manage them Exponential growth gets limited by negative loops kicking in/becoming dominant lt later on 10
11 Limits of Causal Loop Diagrams Causal loop diagrams (CLDs) help in capturing mental models, and showing interdependencies and feedback processes. CLDs cannot capture accumulations (stocks) and flows help in determining detailed dynamics Stocks, Flows and Feedback are central concepts in System Dynamics
12 Stocks Stock Stocks are accumulations, aggregations, summations over time Inflow Stock Outflow Stocks characterize/describe the state of the system Stocks change with inflows and outflows Stocks provide memory and give inertia by accumulating past inflows; they are the sources of delays. Stocks, by accumulating flows, decouple the inflows and outflows of a system and cause variations such as oscillations over time. Stock Flow Valve (flow regulator) Source or Sink
13 Mathematics tics of Stocks Stock and flow diagramming were based on a hydraulic metaphor Inflow Stock Outflow Stocks integrate their flows: Inflow Stock The net flow is rate of change of stock: Outflow Ref: Figure 62, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
14 Stocks and Flows Examples Field Stocks Flows Mathematics, Physics and Engineering Chemistry Integrals, States, State variables, Stocks Reactants and reaction products Derivatives, Rates of change, Flows Reaction Rates Manufacturing Buffers, Inventories Throughput Economics Accounting Levels Stocks, Balance sheet items Rates Flows, Cash flow or Income statement items Ref: Table 61, J. Sterman, Business Dynamics: Systems Snapshot Test: Thinking and Modeling for a complex world, McGraw Hill, 2000 Freeze the system in time things that are measurable in the snapshot are stocks.
15 Example 20 Net flow (units/second) 10 0 Ref: Figure 72, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill,
16 Example 100 Flows (units/time) In flow Out flow Ref: Figure 74, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill,
17 Flow Rates Model systems as networks of stocks and flows linked by information feedbacks from the stocks to the rates. Rates can be influenced by stocks, other constants (variables that change very slowly) and exogenous variables (variables outside the scope of the model). Stocks only change via inflows and outflows. Constant Net rate of change Net rate of change Exogenous variable Stock Stock 17
18 Auxiliary Variables Net birth rate Food?? Population Ref: Figure 8?, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000 Auxiliary variables are neither stocks nor flows, but intermediate concepts for clarity Add enough structure to make polarities clear 18
19 Aggregation and Boundaries I Identify main stocks in the system and then flows that alter them. Choose a level of aggregation and boundaries for the system Aggregation is number of internal stocks chosen Boundaries show how far upstream and downstream (of the flow) the system is modeled dld 19
20 Aggregation and Boundaries II One can challenge the clouds, i.e. make previous sources or sinks explicit. We can disaggregate our stocks further to capture additional dynamics. Stocks with short residence time relative to the modeled time horizon can be lumped together Level of aggregation depends on purpose of model It is better to start simple and then add details. 20
21 From Structure to Behavior The underlying structure of the system defines the time based behavior. Consider the simplest case: the state of the system is affected by its rate of change. Ref: Figure 81, & 82 J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000 Net increase rate R Net rate of change State of the system Stock Images by MIT OpenCourseWare.
22 Population Growth Consider the population Model: Net birth rate Population The mathematical representation of this structure is: Fractional net birth rate R Net birth rate = fractional birth rate * population Ref: Figure 82, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000 Note: units of b, fractional growth rate are 1/time
23 Phase Plots Plots for Exponential Growth Phase plot is a graph of system state vs. rate of change of state ds/dt = Net Inflow Rate = gs Phase plot of a first order, linear positive feedback system is a straight line If the state of the system is zero, the rate of change is also zero Net inflow rate (units/time) 1 g The origin however is an unstable equilibrium. Ref: Figure 83, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, Unstable equilibrium State of the system (units) 23
24 Time Plots Fractional growth rate g = 0.7%/time unit Initial state state = 1. State doubles every 100 time units Every time state of the system doubles, so too does the absolute rate of increase Ref: Figure 84, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000 State of the system (units) Net inflow (units/time) t = 700 t = 800 t = 900 t = State of system (units) Behavior Net inflow (right scale) Time State of the system (left scale) Net inflow (units/time) Images by MIT OpenCourseWare. 24
25 Rule of 70 Exponential growth is one of the most powerful processes. The rate of increase grows as the state of the system grows. It has the remarkable property that the state of the system doubles in fixed period of time. If the doubling time is say 100 time units, it will take 100 units to go from 2 to 4, and another 100 units to go from 1000 to 2000 and so on. To find doubling time:
26 Negative Feedback and Exponential Decay First order linear negative feedback systems generate exponential decay The net outflow is proportional to the size of the stock The solution is given by: S(t) = S dt o e S state of the system B Net outflow rate Fractional decay rate d Examples: Net Inflow = Net Outflow = d*s d: fractional decay rate [1/time] Ref: Figure 86, J. Sterman, Business Dynamics: Systems Reciprocal of d is average lifetime Thinking and Modeling for a complex world, McGraw Hill, 2000 units in stock.
27 Phase Plot for Exponential Decay y In the phase plot, the net rate of change is a straight line with negative slope The origin is a stable equilibrium, a minor perturbation in state S increases the decay rate to bi bring system back to zero deviations from the equilibrium are self correcting Net Inflow Rate =  Net Outflow Rate = ds Net inflow rate (units/time) 0 State of the system (units) 1 Stable equilibrium d The goal in exponential decay is implicit and equal to zero Ref: Figure 87, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
28 Negative Feedback with Explicit Goals In general, negative loops have non zero goals Examples: The corrective action determining net flow to the state of the system is : Net Inflow = f (S, S*) AT adjustment time Net inflow rate  ds/dt General Structure B S state of the system Discrepancy (S*  S)  S* desired state of the system Simplest formulation is: Net Inflow = Discrepancy/adjustment time = (S* S)/AT AT: adjustment time is also known as time constant for the loop Ref: Figure 89, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
29 Phase Plot for Negative Feedback with Non Zero Goal In the phase plot, the net rate of change is a straight line with slope 1/AT Net Inflow Rate = Net Outflow Rate = (S*  S)/AT The behavior of the negative loop with an explicit goal is also exponential decay, in which the state reaches equilibrium when S=S* If the initial state is less than the desired state, the net inflow is positive and the state increases (at a diminishing rate) until S=S*. If the initial state is greater than S*, the net inflow is negative and the state falls until it reaches S* Net inflow rate (units/time) 0 11/AT Ref: Figure 810, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000 S* Stable equilibrium State of the system (units)
30 Half Lives Exponential decay cuts quantity remaining in half in fixed period of time The half life is calculated in similar way as doubling time. The system state as a function of time is given by: State of system Initial Gap S(t) = S * (S ( * S 0 )e Desired State The exponential term decays from 1 to zero as t tends to infinity. Half life is given by value of time t h : Gap Remaining t /τ
31 Time Constants and Settling Time For a first order, linear system with negative feedback, the system reaches 63% of its steady state value in one time constant, and reaches 98% of its steady state value in 4 time constants. The steady state is not reached technically in finite time because the rate of adjustment keeps falling as the desired state is approached. Time Fraction of Initial Gap Remaining Fraction of Initial Gap Corrected 0 e 0 0 = = 0 τ e 1 = e 1 = τ e 2 = e 2 = τ e 3 = e 3 = τ e 4 = e 4 = τ e 5 = e 5 = Fraction of initial gap remaining exp(1/at) exp(1/at) exp(2/at) exp(3/at) 0 0 1AT 2AT 3AT 1  exp(2/at) Time (multiples of AT) 1  exp(3/at) Ref: Figure 812, J. Sterman, Business Dynamics: Systems Thinking and Modeling for a complex world, McGraw Hill, 2000
32 MIT OpenCourseWare ESD.00 Introduction to Engineering Systems Spring 2011 For information about citing these materials or our Terms of Use, visit:
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