Lets Give More Emphasis to Opportunities in Complex Enterprise Environments Brian E. White, Ph.D. Director, Systems Engineering Process Office The MITRE

Let’s Give More Emphasis to Opportunities in “Complex” “Enterprise” Environments

Brian E. White, Ph.D.Director, Systems Engineering Process Office

The MITRE Corporation

Conference on Advanced Risk Management and

Its Applications to Systems Engineering

8-9 November 2007

Hosted by the Hampton Roads Area Chapter ofInternational Council on Systems Engineering (HRA INCOSE)

Omni Hotel, Newport News, VA©2007 The MITRE Corporation. All rights reserved.

2

Takeaways

“Mandatory” reading: N. N. Taleb, The Black Swan—Impact of the HIGHLY IMPROBABLE, Random House, New York, 2007http://www.amazon.com/Black-Swan-Impact-Highly Improbable/dp/1400063515/ref=pd_bbs_sr_1/102-6890811-9526543?ie=UTF8&s=books&qid=1192905688&sr=8-1

Complex systems include people, and we must bring more humility to engineering the environment of complex. systems, e.g., enterprises.

A complementary mindset is critical to furthering progress in systems engineering.

In complex enterprises, it’s all about opportunities because one is going “off track” all the time. The biggest risk is in not pursuing opportunities.

3

Introduction* Many of us (in diverse fields) are concerned about risk management.

– So much so that risks are identified early and often, and mitigation techniques are aggressively pursued.

– In contrast one does not hear much about opportunity management, e.g., [de Weck and Eckert, 2007], [Chang and Backus, 2007], and [Youssef, 2005]

– We should ask ourselves In our complex enterprise environments, do traditional methods of handling

risk (and opportunity?) carry over, or Should we be more concerned about potential changes in the way we view the

problems?

Enterprise Opportunity and Risk (terminology can be confusing)– The impression from aerospace and/or “military-industrial complex”

documentation and anecdotal evidence is that there is a disproportional emphasis on risk as opposed to opportunity, e.g.,

[Edmondson, et al., 2005] and [Roberto, et al., 2006] Presenter at 5 April 2007 complex system workshop: “… there are lots of

opportunities to do it wrong”(!)

– What about other commercial, etc., domains, e.g., venture capitalists? – What is the sense of this risk management/systems engineering group?

The institutional biases are:

Venture capital domain: Stop the program if seed money doesn’t produce something useful.

Department of Defense acquisition: Continue the program even though there are serious problems.

___________* [White, 2007a]

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Introduction (Concluded)* We assert new perspectives on this topic for enterprises

– There is duality in treating risks and opportunities.– Be aggressive with opportunity and accepting of risk.– The greatest enterprise risk may be in not pursuing enterprise

opportunities.– Complexity theory can inform us about effective approaches to

opportunity management.– An opportunity mindset can lead to emergent innovations.

___________* [White, 2007a]

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Relative Importance of Opportunity*

System of Systems View

Systems View

Enterprise View

Risk

Risk

Opportunity

Opportunity

UnknownUn-assessableUncertainty

The minimum goal of this talk is to raise your sensitivity level for proactively

pursuing opportunities at all engineering views.

See Notes Page___________* [White, 2007a] and [White, 2007]

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{View} = {Scope, Granularity, Mindset, Timeframe}*

Time

Granularity (e.g., resolution)

Scope(e.g., FoV)

Small

Large

Fine

Inaccessible Region(where a given humancannot conceptualize

Coarse

Mindset(e.g., cognitive focus)

Accessible Region (where that human can conceptualize)

A change in a mind’s focus results in a change of View!

No View change can take one beyond this limit!

___________* [White, 2007]

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Risk/Opportunity Representation on Probability/Impact Grid*

Low

Medium

High

Medium

MediumMedium High

HighHigh

LowLow

Low

Positive ImpactBenefit of Success

Bs

Negative ImpactConsequence of Failure

Cf

ProbabilityPo

ProbabilityQo

Risks Opportunities

“Attention Arrow”

See Notes Page___________* [White, 2007a] after [Hillson, 2004], p. 126

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What Are Consequences of Failure?*

___________

* [White, 2007a] from [Garvey, 2005], p. 7

Condition Present 1

Consequence Event 111

Risk Event 11





Root Cause

CONDITIONEvent B

IF this Risk Event A Occurs

The region bounded by this space is Probability (A|B)

Current test plans are focused on the components of the subsystem and not on the subsystem as a whole.

Subsystem may not be fully tested when integrated into the system for full-up system-level testing.

Consequences of failure are undesirable events that degrade the performance or

capability of a system, SoS, or Enterprise.

THEN these are the consequences

Full-up system will revealunanticipated performance shortfalls

Subsystem will have to accommodateunanticipated changes in subsequent buildhardware/software requirements which willaffect development cost and schedules

User will not accept delivery of subsystemhardware/software without fixes

Subsystem will reveal unanticipated performance shortfalls

Subsystem will have to incorporate late fixes to tested software baseline

The Risk Statement: An Illustration of CONDITION-IF-THEN

See Notes Page

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What is Opportunity?* Opportunities are events or occurrences that assist a program in

achieving its cost, schedule, or technical performance objectives. In the larger sense, explored opportunities can enhance or

accomplish the entire mission. Opportunity also is associated with uncertainty and impact. There is a duality or parallelism to risk that can be applied. For an opportunity, let Qo be the probability of occurrence, Bs, the

benefit of success, and Ee, estimated enhancement.

We can pose the simple formula:

Ee = Qo × Bs[This is expected benefit!]

Probability = 0 < Qo < 1

B

enef

it =

0

<

Bs <

∞

Opportunity Assessment Ao = {Qo, Bs}An interpretation: No Gain Worthwhile Pain Golden Opportunity Windfall Euphoria

See Notes Page___________* [White, 2007a]

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Opportunity Classification Example*

___________

* [White, 2007a] after [Garvey, 2005], p. 8

Bs

Qo

Opportunity

Opportunity

Opportunity

“Opportunity Averse” System Profile

“Opportunity Seeking” System Profile

See Notes Page

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Thoughts About Opportunity and Risk Concerning TSE, SoS Engineering, and ESE or CSE*

Think about opportunity/risk with respect to a complex system’s environment in addition to the system per se.– There may be many more opportunities in the system’s environment.

– The pursuit of these opportunities could reduce the system’s “stress”.

– Environmental risks seem less important than the opportunities.

– Enterprise-view opportunity action and risk avoidance can be viewed with a philosophy of “nothing ventured, nothing gained”.

– Downside risk is about not incurring “damage” that might stifle the aforementioned opportunities.

Compare and contrast TSE and CSE concepts.– A complex system (and enterprise) is “open”.

– This suggests a predisposition for opportunities.

– One should “open” the system further to create more emergent behavior.

– Be more aggressive with identifying, exploring, and developing opportunities than in TSE.


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Thoughts About Opportunity and Risk Concerning TSE, SoS Engineering, and ESE or CSE (Concluded)*

Enterprise risks can be mitigated by creating a management process that has built-in abilities to

– Quickly assess whether emergent behavior is desirable

– Encourage desirable behavior

– Discourage undesirable behavior

– Encourage greater acceptance of risks

Stevens: Messy frontier– Political engineering (power, control…)

– High risk, potentially high reward

– Foster cooperative behavior

One may learn from researching what economists do about opportunity and risk at multi-views of analysis, i.e., macroeconomics and microeconomics.**

In summary– Opportunities for intervening in enterprise environments are great.

– The greatest enterprise risk may be in allowing this process to atrophy.

See Notes Page_________** [Kuras, 2004]

___________* [White, 2007a]

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Opportunities and Risks in “Establish Rewards”* Suppose a suitable outcome space has been identified. Autonomous agents will develop specific outcomes taking

advantage of opportunities. There is risk in developing products that

– May not become outcomes

– Become less desirable outcomes These risks are either not rewarded or are rewarded less. Because a reward is granted to many outcomes, agents may

pursue opportunities more aggressively than mitigating the risks of not achieving outcomes.

Risk mitigation could be reduced to ordering outcomes according to rewards.

This ordering might be pursued in conjunction with other autonomous agents because rewards are granted only to targeted populations of agents.

The hypothesis that opportunities would be treated more aggressively than risks still needs validation.


14

Opportunities in “Characterize Continuously”* This CSE activity is the continual generation and refinement of

complex-system characterizations. Continuous Characterization is crucial for autonomous agents to independently develop metrics to guide their local decision making to be congruent.

The specific outcomes used as the basis for Judging should be characterized, as should the rationale that eventually explains the subsequent Judging decisions.

Rewards (and perhaps Outcome Spaces) initially should be characterized with succinct “bumper-sticker” labels. The U.S. Army motivated a tremendous spurt forward with the visionary, “Own the Night”.

Pithiness encourages opportunities for inconsistencies in how Rewards (and Outcome Spaces) are interpreted. To the extent that consistency matters, however, a complex system will benefit from continually developing and espousing more detailed and complete characterizations.

However, in complex-system evolution, characterizations cannot be too refined. New Outcome Spaces may need to be added to the characterizations, or their new possibilities will not be explored.


15

Comparing TSE and SoS Risk Management*

___________* [White, 2007a] from [Garvey, 2005], p. 12

Figure 9 (edited). Three Color Comparative Assessment Scheme

In a SoS, this action step requires a

significant increaseeffort and scope as compared to a TSE

environment


modest increaseeffort and scope as compared to a TSE

environment


similareffort and scope as compared to a

TSE environment

Process

Tools/

Constructs

In a SoS environment, tools and/or analytical constructs similar to those applied in TSE environments can be used; however, they

require

significant changes to their designs or significant extensions

to their underlying logicto be properly applied in an SoS environment.

In some areas, new tools and/or analytical constructs may also be needed.

In a SoS environment, tools and/or analytical constructs similar to those applied in TSE environments can be used; however,

they require

modest changes to their designs or modest extensions

to their underlying logicto be properly applied in an SoS

environment.

In a SoS environment,

similartools and/or analytical constructs

can be used with few (if any) modification as they are applied in a

TSE environment.

ororor


significant increaseeffort and scope as compared to a TSE

environment


modest increaseeffort and scope as compared to a TSE

environment


similareffort and scope as compared to a

TSE environment

Process

Tools/

Constructs

In a SoS environment, tools and/or analytical constructs similar to those applied in TSE environments can be used; however, they

require

significant changes to their designs or significant extensions

to their underlying logicto be properly applied in an SoS environment.

In some areas, new tools and/or analytical constructs may also be needed.

In a SoS environment, tools and/or analytical constructs similar to those applied in TSE environments can be used; however,

they require

modest changes to their designs or modest extensions

to their underlying logicto be properly applied in an SoS

environment.

In a SoS environment,

similartools and/or analytical constructs

can be used with few (if any) modification as they are applied in a

TSE environment.

ororor

G Y R

See Notes Page

16

Comparing TSE, and SoS and ESE Opportunity Management*Assessment Action Steps and Substeps Assessment Action Steps and Substeps

Yellow Step 1 Prepare Red Step 1 PrepareY: Action 1 Commit Resources R: Action 1 Commit ResourcesY: Action 2 Form the Team R: Action 2 Form the TeamY: Action 3 Know the Mission R: Action 3 Know the MissionR: Action 4 Think Opportunities Y: Action 4 Think Opportunities

Yellow Step 2 Identify the Opportunities Yellow Step 2 Identify the OpportunitiesY: Action 1 Establish Team R: Action 1 Establish TeamY: Action 2 Develop Understanding R: Action 2 Develop UnderstandingY: Action 3 Identify Opportunities Y: Action 3 Identify OpportunitiesG: Action 4 Classify Opportunities G: Action 4 Classify OpportunitiesG: Action 5 Write Opportunity Statements G: Action 5 Write Opportunity StatementsR: Action 6 Correlate Related Opportunities Y: Action 6 Correlate Related Opportunities

Yellow Step 3 Assess and Prioritize Opportunities Red Step 3 Assess and Prioritize OpportunitiesY: Action 1 Impact Assessment R: Action 1 Impact AssessmentG: Action 2 Probability Assessment Y: Action 2 Probability AssessmentR: Action 3 Timeframe Assessment R: Action 3 Timeframe AssessmentY: Action 4 Reassess Opportunities R: Action 4 Reassess OpportunitiesY: Action 5 Rank Opportunities R: Action 5 Rank OpportunitiesG: Action 6 Coarse Sort; Identify Handling Bands Y: Action 6 Coarse Sort; Identify Handling Bands

Green Step 4 Decide on Handling Options Yellow Step 4 Decide on Handling OptionsG: Action 1 Identify Options within Each Opportunity Band Y: Action 1 Identify Options within Each Opportunity BandG: Action 2 Easy Opportunities Y: Action 2 Easy OpportunitiesY: Action 3 Hard Opportunities R: Action 3 Hard OpportunitiesY: Action 4 Assign OPRsG R: Action 4 Assign OPRsGG: Action 5 Update Opportunity Database G: Action 5 Update Opportunity Database

Yellow Step 5 Establish Handling Plans Red Step 5 Establish Handling PlansY: Action 1 Develop Plans and Estimates R: Action 1 Develop Plans and EstimatesR: Action 2 Review and Approve R: Action 2 Review and ApproveY: Action 3 Fund, Direct, Integrate R: Action 3 Fund, Direct, Integrate

Yellow Step 6 Implement Opportunity Handling Red Step 6 Implement Opportunity HandlingY: Action 1 Finalize Opportunity Management Plan R: Action 1 Finalize Opportunity Management PlanY: Action 2 Provide Mechanisms to Monitor Y: Action 2 Provide Mechanisms to MonitorY: Action 3 Implement Handling Plans R: Action 3 Implement Handling PlansY: Action 4 Monitor Progress Y: Action 4 Monitor Progress

Green Step 7 Monitor Handling Plans Yellow Step 7 Monitor Handling PlansG: Action 1 Periodically Review Handling Plans Y: Action 1 Periodically Review Handling PlansY: Action 2 Modify or Stop, If Required R: Action 2 Modify or Stop, If RequiredG: Action 3 Retire Opportunities Y: Action 3 Retire Opportunities

SoS Opportunity Management Enterprise Opportunity Management

G = greenY = yellowR = red


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Concluding Remarks* The greatest enterprise risk may be in not pursuing enterprise

opportunities.

There is duality– In treating risks and opportunities

– Between systems and enterprises

Opportunity (as well as risk) management is a “team sport”.– But ESE is the “big leagues” for opportunity management.

Keep in mind there are unknowns and unknowables.

Opportunities in ESE abound!

Qualitative assessments of opportunity management– Tend to be more difficult for enterprises than for SoS or systems

– Could easily change after learning more about ESE

Our principal hypothesis: In ESE, be aggressive with opportunity and accepting of risk.

– This is just the opposite of what seems to be the case in TSE!

– Nevertheless, validation from actual case studies should be sought.


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List of References[Brooks, 1995] Brooks, F. P., The Mythical Man-Month: Essays on Software Engineering, 20th Anniversary Edition (Paperback), Addison Wesley, 2nd (anniversary) expanded edition, 2nd corrected printing, 1995 http://www.amazon.com/gp/product/customer-reviews/0201835959/ref=cm_cr_dp_pt/002-1403359-6272017?%5Fencoding=UTF8&n=283155&s=books

[Chang and Backus, 2007] Chang, S.-J. F., and G. R. Backus, “Applying Real Options to Flexible Systems Engineering: A Proof-of-Concept Study on Capability Acquisition of Tactical Data Links,” IEEE Systems Conference, Honolulu, HI, 9-12 April 2007

[Edmondson, et al., 2005] Edmondson, A. C., M. A. Roberto, R. M. J. Bohmer, E. M. Ferlins, and L. R. Feldman, “The Recovery Window: Organizational Learning Following Ambiguous Threats,” Chapter 12, Organization at the Limit: lessons from the Columbia disaster, Blackwell Publishing Ltd, Malden, MA, 2005, pp. 220-245

[Garvey, 2005] Garvey, P. R., “System-of-Systems Risk Management: Perspectives on Emerging Process and Practice,” MP 04B0000054, MITRE Product, The MITRE Corporation, 2005 http://sepo1.mitre.org/ese_wg/library/sos_risk.html

[Haberfellner and de Weck, 2005] Haberfellner, R., and O. de Weck, “Agile Systems-Engineering versus Agile-Systems Engineering,” INCOSE Symposium, Rochester, NY, 10-15 July 2005

[Hillson, 2004] Hillson, D., Effective Opportunity Management for Projects, Risk Doctor & Partners, Petersfield, Hampshire, United Kingdom, Marcel Dekker, Inc., New York, 2004

[Kuras, 2004] Kuras, M. L., personal communication, 2004

[Kuras and White, 2005] Kuras, M. L., and B. E. White, “Engineering Enterprises Using Complex-System Engineering,” INCOSE Symposium, Rochester, NY, 10-15 July 2005

[Kuras and White, 2006] Kuras, M. L., and B. E. White, “Complex Systems Engineering Position Paper: A Regimen for CSE,” Conference on Systems Engineering Research (CSER), Los Angeles, CA, 7-8 April 2006

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List of References (Concluded)[Roberto, et al., 2006] Roberto, M. A., R. M. J. Bohmer, and A. C. Edmondson, “Facing Ambiguous Threats,” Harvard Business Review, November 2006, pp. 106-113

[Taleb, 2007] N. N. Taleb, The Black Swan—Impact of the HIGHLY IMPROBABLE, Random House, New York, 2007 http://www.amazon.com/Black-Swan-Impact-Highly Improbable/dp/1400063515/ref=pd_bbs_sr_1/102-6890811-9526543?ie=UTF8&s=books&qid=1192905688&sr=8-1

[de Weck and Eckert, 2007] de Weck, O., and C. Eckert, “A Classification of Uncertainty for Early Product and System Design,” ESD-WP-2007-10, Working Paper, Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, February 2007

[White, 2005] White, B. E., “A Complementary Approach to Enterprise Systems Engineering,” National Defense Industrial Association, 8th Annual Systems Engineering Conference, San Diego, CA, October 24-27, 2005

[White, 2006] White, B. E., “Enterprise Opportunity and Risk,” INCOSE Symposium, Orlando, FL, 9-13 July 2006

[White, 2006a] White, B. E., “Fostering Intra-Organizational Communication of Enterprise Systems Engineering Practices,” National Defense Industrial Association, 9th Annual Systems Engineering Conference, San Diego, CA, 23-26 October 2006

[White, 2007] White, B. E., “On Interpreting Scale (or View) and Emergence in Complex Systems Engineering,” 1st Annual IEEE Systems Conference, Honolulu, HI, 9-12 April 2007

[White, 2007a] White, B. E., “Let’s Talk More About Opportunities in Uncertainty Management!,” Project Risk Symposium, San Francisco, CA, 16-18 May 2007

[Youssef, 2005] Youssef, J., “The Upside of Risk Management,” ARM Customer Conference, Arlington, VA, 19-20 September 2005

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Back Up Charts

21

Some Quotes from The Black Swan Definition

– “... a Black Swan ... is an event … [1] it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. [2] it carries an extreme impact. [3] … human nature makes … it explainable and predictable.” pp. xvii-xviii

Asymmetry– “Black Swan logic makes what you don't know far more relevant

than what you do know. Consider that many Black Swans can be caused and exacerbated by their being unexpected.” p. xix

– “The Black Swan asymmetry allows you to be confident about what is wrong, not about what you believe is right.” p. 192

Risk– “… owing to the shallowness of our intuitions, we formulate our

risk assessments too quickly.” p. 97

Statistics– “… principle: the rarer the event, the higher the error in our

estimation of its probability … the Gaussian bell curve sucks randomness out of life—which is why it's so popular. We like it because it allows certainties! How? Through averaging ...” p. 237

22

Some Quotes from The Black Swan (Continued)

Narration– “… the problem of silent evidence. … to the point of blindness

to reality. It is why we fall for the problem of induction, why we confirm. … We respect what has happened, ignoring what could have happened.” pp. 131-132

– “There is a blind spot: when we think of tomorrow we do not frame it in terms of what we thought about yesterday or the day before yesterday. Because of this introspective defect we fail to learn about the difference between our past predictions and the subsequent outcomes. When we think of tomorrow, we just project it as another yesterday.” p. 193

Decisions– “Don't cross a river if it is four feet deep on average. … The

policies we need to make decisions on should depend far more on the range of possible outcomes than on the expected final number.” p. 161

Mindset– “Know how to rank beliefs not according to their plausibility

but by the harm they may cause.” p. 201

23

Some Quotes from The Black Swan (Concluded) Opportunity

– “… invest in preparedness, not in prediction. Remember that infinite vigilance is not possible. … Seize any opportunity, or anything that looks like opportunity. They are rare, much rarer than you think. … you need to be exposed to [positive Black Swans]. Many people do not realize that they are getting a lucky break in life when they get it. ... Work hard … in chasing such opportunities and maximizing exposure to them. ... casual chance discussions at cocktail parties usually lead to big breakthroughs …” pp. 208, 209

– “I worry less about embarrassment than about missing an opportunity.” p. 296

Uncertainty– “… in order to make a decision you need to focus on the

consequences (which you can know) rather than the probability (which you can't know) is the central idea of uncertainty.” p. 211

Humility– “As events present themselves to us, we compare what we see to

what we expected to see. It is usually a humbling process, particularly for someone aware of the narrative fallacy, to discover that history runs forward, not backward.” p. 268

24

The Black Swan “Landscape”Mediocristan Extremistan

Nonscalable Scalable

Mild or type 1 randomness Wild (even superwild) or type 2 randomness

The most typical member is mediocre The most "typical" is either giant or dwarf, i.e., there is no typical member

Winners get a small segment of the total pie Winner-take-almost-all effects

Example: audience of an opera singer before the gramaphone Today's audience for an artist

More likely to be found in our ancestral environment More likely to be round in our modern environment

Impervious to the Black Swan Vulnerable to the Black Swan

Subject to gravity There are no physical constraints on what a number can be

Corresponds (generally) to physical quantities, i.e., height Corresponds to numbers, say, wealth

As close to utopian equality as reality can spontaneously deliver Dominated by extreme winner-take-all inequality

Total is not determined by a single instance or observation Total will be determined by a small number of extreme events

When you observe for a while you can get to know what's going on It takes a long time to know what's going on

Tyranny of the collective Tyranny of the accidental

Easy to predict from what you see and extend to what you do not see

Hard to predict from past information

History crawls History makes jumps

Events are distributed* according to the "bell curve" (the GIF) or its variations

The distribution is either Mandelbrotian "gray" Swans (tractable scientifically) or totally intractable Black Swans

* "What I call 'probability distribution' here is the model used to calculate the odds of different events, how they are distributed. When I say that an event is distributed according to the 'bell curve,' I mean that the Gaussian bell curve (after C. F. Gauss; more on him later) can help provide probabilities of various occurrences."

N. N. Taleb, The Black Swan—Impact of the HIGHLY IMPROBABLE, Random House, New York, 2007, Table 1, p. 36.

25

Complexity Terms: View, Complexity, Emergence*

View: A human conceptualization consisting of scope, granularity, mindset, and timeframe

Complexity: Description of the ultimate richness of an entity that – Continuously evolves dynamically through self-organization of

internal relationships – Requires multi-view analysis to perceive different non-repeating

patterns of its behavior – Defies methods of pre-specification, prediction, and control

Note: Complexity as really a continuum extending from its lowest degree, complication, say, to its higher degree, intended here.

Emergence: Something unexpected in the collective behavior of an entity within its environment, not attributable to any subset of its parts, that is present (and observed) in a given view and not present (or observed) in any other view.– Notes: Some people employ a broader definition where things that

emerge can be expected as well as unexpected. Emergence can have benefits or consequences.

___________

* [White, 2006a] and [White, 2007]

26

System Terms: System and SoS*

System: An interacting mix of elements forming an intended whole greater than the sum of its parts.– Features: These elements may include people, cultures,

organizations, policies, services, techniques, technologies, information/data, facilities, products, procedures, processes, and other human-made (or natural) entities. The whole is sufficiently cohesive to have an identity distinct from its environment.

System of Systems (SoS): A collection of systems that functions to achieve a purpose not generally achievable by the individual systems acting independently.– Features: Each system can operate independently (in the same

environment as the SoS) and is managed primarily to accomplish its own separate purpose.

___________

* [White, 2006a]

27

System Terms (Concluded): Complex System, CAS, and Enterprise*

Complex System: An open system with continually cooperating and competing elements. – Features: Continually evolves and changes according to its

own condition and external environment. Relationships among its elements are difficult to describe, understand, predict, manage, control, design, and/or change.

Notes: Here “open” means free, unobstructed by artificial means, and with unlimited participation by autonomous agents and interactions with the system’s environment.

Complex Adaptive System (CAS): Identical to a complex system.

Enterprise: A complex system in a shared human endeavor that can exhibit relatively stable equilibria or behaviors (homeostasis) among many interdependent component systems.– Feature: An enterprise may be embedded in a more inclusive

complex system.

___________

* [White, 2006a]

28

Engineering Terms: Engineering, Enterprise Engineering, and Systems Engineering*

Engineering: Methodically conceiving and implementing viable solutions to existing problems.

Enterprise Engineering: Application of engineering efforts to an enterprise with emphasis on enhancing capabilities of the whole while attempting to better understand the relationships and interactive effects among the components of the enterprise and with its environment.

Systems Engineering: An iterative and interdisciplinary management and development process that defines and transforms requirements into an operational system.– Features: Typically, this process involves environmental,

economic, political, social, and other non-technological aspects. Activities include conceiving, researching, architecting, utilizing, designing, developing, fabricating, producing, integrating, testing, deploying, operating, sustaining, and retiring system elements.

___________

* [White, 2006a]

29

Engineering Terms (Concluded): TSE, ESE, and Complex Systems Engineering*

Traditional Systems Engineering (TSE): Systems engineering but with limited attention to the non-technological and/or complex system aspects of the system.– Feature: In TSE there is emphasis on the process of selecting and

synthesizing the application of the appropriate scientific and technical knowledge in order to translate system requirements into a system design.

Enterprise Systems Engineering (ESE): A regimen for engineering “successful” enterprises. – Feature: Rather than focusing on parts of the enterprise, the

enterprise systems engineer concentrates on the enterprise as a whole and how its design, as applied, interacts with its environment.

Complex Systems Engineering (CSE): ESE that includes additional conscious attempts to further open an enterprise to create a less stable equilibrium among its interdependent component systems.– Feature: The deliberate and accelerated management of the natural

processes that shape the development of complex systems.

___________

* [White, 2006a]

30

Example System Profiles*

Po

Cf

0

1

1

positive slopes

negative slopes

ΔPo

ΔPo

ΔCf

< ΔCf

1

23risk event number

risk averse profile(decreasing slope)

risk seeking profile(increasing slope)

risk neutral profile(constant slope)


31

What Can One Do to Engineer a Complex Systems Environment?*

Analyze and shape the environment: Guide the complex-system's self-directed development. This depends on the nature of the system and its environment. No portion of the environment can be directly controlled in a persistent fashion.

Tailor developmental methods to specific regimes and scales: Any complex-system operates in multiple regimes and at multiple scales. The operational regime is directly associated with the purposes or mission of the whole system. The developmental regime is associated with changes in the system. These two regimes cannot be sufficiently isolated for a complex-system.

Identify or define targeted outcome spaces: Outcome spaces are large sets of possible partial outcomes at specific scales and in specific regimes. The complex-system itself will choose the exact combinations of partial outcomes that it realizes.

Establish rewards (and penalties): Establish rewards (and penalties) that are intended to influence the behavior of individual (but not specific) autonomous agents at one or more scales and regimes to influence agent outcomes.

___________* [Kuras and White, 2006] See Notes Page

32

What Can One Do to Engineer a Complex Systems Environment?* (Concluded) Judge actual results and allocate rewards: Consider and

judge the actual outcomes in many or all of the regimes and scales in terms of targeted outcome spaces. Then allocate rewards to the most responsible agents, whether they were pursuing those rewards or not. Do this in ways that preserve or even increase the opportunity for more new results.

Formulate and apply developmental stimulants: Use methods that increase the number of, or the intensity and persistence of, interactions among autonomous agents. Specific forms of this method depend on the phase of the developmental cycle of a capability that is being addressed.

Characterize continuously: Aim at gathering information at multiple scales and in multiple regimes pertinent to Outcome Spaces and making it available to the autonomous agents.

Formulate and enforce fitness regulations (policing): For example, initiate procedures aimed at detecting and screening changes so that fitness is maintained; that monitor characteristic periods; and that inhibit or negate changes that increase characteristic periods.

___________* [Kuras and White, 2006] See Notes Page

Documents

Lets Give More Emphasis to Opportunities in Complex Enterprise Environments Brian E. White, Ph.D. Director, Systems Engineering Process Office The MITRE