mpd 575 design for satisficing (dfs) team bbcd cohort #9 b rad kenoyer b ill parran c hintan ved d...

MPD 575Design For SATISFICING (DFS)

Team BBCDCohort #9

Brad Kenoyer

Bill Parran

Chintan Ved

Dawoud AlQasrawi

Design For SATISFICING (DFS)

• Overview & Definition• Objectives of System Engineering (SE)• Satisficing and Ford’s Quality Triangle • Satisficing and Existing Products• Satisficing and High Product Quality• Satisficing and Superior Purchase and Service Experience• Examples• Discussion

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• Satisficing: The selection of an acceptable or satisfactory solution that meet an agents minimum aspiration-level or threshold , a threshold, under which solutions are deemed unacceptable . A satisficing solution may or may not be an optimal economic solution.

Reference: http://en.wikipedia.org/wiki/Satisficing#References

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• The word satisfice was coined by Herbert Simon. He pointed out that human beings lack the cognitive resources to maximize: we usually do not know the relevant probabilities of outcomes, we can rarely evaluate all outcomes with sufficient precision, and our memories are weak and unreliable. A more realistic approach to rationality takes into account these limitations: This is called bounded rationality.

http://en.wikipedia.org/wiki/Herbert_Simon

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Design For SATISFICING (DFS)• Do not try to produce optimal designs, because for

complex systems this is impossible. • The key to successful design is “the replacement of

the goal of maximization with the goal of satisficing, of finding a course of action that is 'good enough.' ... Since the [designer] ... has neither the senses nor the wits to discover an 'optimal' path – even assuming the concept of optimal to be clearly defined – we are concerned only with finding a choice mechanism that will lead it to pursue a 'satisficing' path, a path that will permit satisfaction at some specified level of all its needs.” - Herb Simon, 1957

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Objectives of System Engineering (SE)

• In a nutshell, SE is all about making the best dang system possible

• The SE must find the best balance of the critical system attributes– “The best is the enemy of the good”– “Systems engineering is the art of good enough”

• SE is about satisficing - MPD 510 Systems Engineering - Weaver Topic 1 20060911



• When delivering any product, there are essentially three main components that must be right: product content (what's designed in), product execution (how it's designed to meet customer usage), and the customer interface points.

• This is best demonstrated by Ford's quality triangle.

Ford’s Quality Triangle


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Satisficing in Search

• Threshold: A threshold is imposed with regard to the satisfaction or suitability of a solution. Once a solution meets such a threshold, the search may be considered satisfied.

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• Tradeoffs: More than one consideration (such as fitness or utility) is evaluated when seeking a solution.

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• Comparative: from satisficing evolution, solution utility may be considered comparatively rather than absolutely. This ordinal ranking may further be reduced to the Boolean status of satisfactory and unsatisfactory solutions.

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• Presence of Optimality: also from satisficing evolution, unlike optimization that assumes the presence of a global or locally optimum solution, satisficing makes no such assumptions about the existence of an optimum where one may or may not exist.

Design For SATISFICING (DFS)Example of Exciting & Innovative Products

• Launched in 1991, Explorer defined the modern SUV phenomenon by offering just the right amount of equipment and style

• Sales peaked at just short of 500,000 units per year

http://en.wikipedia.org/wiki/Image:1st_Ford_Explorer_Eddie_Bauer.jpg


Explorer Satisficing Points:• Vehicle not the fastest, most fuel efficient, or

best off road• Offered luxury appointments similar to the

best in class but not unique in its segment• Combination of size, style, content, and price

offered the general package that met a large number of peoples perceived needs without adding unnecessary cost and complexity


SUV / CUV Evolution Satisficing Points:• Customer needs became clearer as the segment evolved• New products were added starting in 2001 (Escape and Edge

shown above)• Unnecessary truck-related parts were shed (full-frame, solid

rear axle) and more comfort features added but basic formula remained the same

http://en.wikipedia.org/wiki/Image:2001-04_Ford_Escape.jpg

http://en.wikipedia.org/wiki/Image:Ford_Edge_Limited.jpg

Design For SATISFICING (DFS)Example of High Product Quality (cont.)

Black & Decker

• Black & Decker is a global manufacturer and marketer of quality power tools and accessories, hardware and home improvement products, and technology based fastening systems.


• Fein defines quality as high performance precise power tool that is durable and exceed the customer expectation.

• B&D defines it as a quality that is good enough; that can perform the job many times but it is not durable and high performance as a Fein.

• Both companies define QUALITY term in different aspect.


• Fein Electric Hacksaw.

It costs

$2,409.75

• B&D corded Cut Saw Kit

It costs

$111.60


• From the price standpoint, it apparent that Quality is not the same in both products.

• Black & Decker certainly satisfy the customers (majority of the customer base) who don’t want to pay too much and at the same time get the job done.


• Superior Purchase and Service Experience– As the attractiveness of product or service

alternatives rises, people experience conflict and, as a result, may put off making a decision, choose the default option, or simply opt out

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• Superior Purchase and Service Experience (cont.)– Research suggests that as the number of

alternatives increases, people simplify their decision making processes by relying on heuristics, they tend to consider fewer alternatives, and process a smaller fraction of the available information regarding those alternatives.

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• Superior Purchase and Service Experience (cont.)– Almost a half century ago, Simon (1955,

1956, 1957) suggested an approach to explaining choice that was more cognizant of human cognitive limitations than rational choice theory.

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• Superior Purchase and Service Experience (cont.)– Simon argued that the presumed goal of

maximization (or optimization) is virtually always unrealizable in real life, owing both to the complexity of the human environment and the limitations of human information processing

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• Superior Purchase and Service Experience (cont.)– He suggested that in choice situations,

people actually have the goal of “Satisficing” rather than Maximizing.

– So, there will be two types of people:• Maximizers (optimizers)• Satisficers

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• Superior Purchase and Service Experience (cont.)– To satisfice, people need only to be able

to place goods on some scale in terms of the degree of satisfaction they will afford, and to have a threshold of acceptability

– A satisficer simply encounters and evaluates goods until one is encountered that exceeds the acceptability threshold

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• Superior Purchase and Service Experience (cont.)– A satisficer thus often moves in the

direction of maximization without ever having it as a deliberate goal

– To satisfice is to pursue not the best option, but a good enough option.

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Design For SATISFICNG (DFS)

• Superior Purchase and Service Experience (cont.)– In one series of studies (Lyengar & Lepper,

2000; see also Iyengar & Lepper, 1999), participants were more likely to purchase exotic jams or gourmet chocolates when they had 6 options from which to choose than when they had 24 or 30, respectively.

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• Superior Purchase and Service Experience (cont.)– And perhaps more importantly, those with

fewer options expressed greater satisfaction with the choices they made.

– The more options there are, the more likely one will make a non-optimal choice.

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Design For SATISFICING (DFS)• Superior Purchase and Service Experience

(cont.)– As an example of high satisficers

customers are the Apple Computers customers, the company provide them with limited numbers of options that fulfill their needs, and because of that Apple customers are more satisfied than any other computer customers.

Reference: Maximizing Versus Satisficing: Happiness Is a Matter of Choice.

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Design For SATISFICING (DFS)Example of Superior Purchase & Service

Experience

Southwest Airlines

Design For SATISFICING (DFS)Example of Superior Purchase & Service

Experience• Company Goal

– Affordable flying for customers – Be a profitable company– Achieve job security for every employee

• Within its own industry group, the company scored first place in such key attributes as: innovation, employee talent, use of corporate assets, social responsibility, quality of management, financial soundness, and long-term investment value according to Fortune Magazine

• Southwest was the first airline to start the trend of no first class; no food other than peanuts, potato chips, or cookies; no assigned seats; no transfers of luggage to other airlines.

• Yet, Southwest Airlines has become the nation’s fourth largest carrier in terms of customer boardings.

• Southwest has 35,000 employees and it serves 59 airports in 58 cities in 30 states. It operates nearly 2,800 flights a day.

Design For SATISFICING (DFS)Example of Superior Purchase &

Service Experience

• On an average Southwest carried over 44 million passengers per year

• While the average cost per passenger of serving meals in the industry is about $5, Southwest’s average cost per passenger is only 20 cents. They passed the savings the customers in terms of reduced airfare.

• Southwest Airlines boasts the best on-time record, best baggage handling, and fewest customer complaints in the airline industry.


Service Experience

• While their competition was focusing on travel flexibility, covering each & every major city in the US – Southwest was focusing on maintaining low cost fares (their goal since day one).

• Oil price hedging (might be luck) certainly helped Southwest in 2007 / 08 CY’s

• Elimination of onboard food and limiting itself to only specific cities helped Southwest satisfy the customers.


Service Experience


• The following tables are examples of Maximizing verses SATISFICING for different Features.

• Tables from W.C. Wimstatt 12/29/05

Design For SATISFICING (DFS)A Comparison of Features: Maximizing vs. Satisficing W. C. Wimsatt 12-29-05

I. FRAMEWORK ISSUES

Feature Maximization Satisficing Comments

1.Conceptual Paradigm

Rationality as logic; (irrationalityas incoherent)choices and theirstructure arerationaland maximal

Rationality as adaptive; May startwith badinformation anddecision rules; needrobust procedurefor improvement(Simon: ProceduralRationality)

“Logic” paradigm fits claim that rational behavior is analytically maximal. Adaptive paradigm is more open to contingent and context-specific designs; see ‘heuristics.’

2.Experimental / Theoretical Paradigm

study Simple [laboratory]cases;Mechanismsremain validwhen extendedto cover complexcases

study Complex[natural] cases;DifferentDegenerate(maximiz.)behaviors mayarise in simple well-defined cases

Key question: do you learn fundamental (basic) laws of interaction in simple cases, or do you see the most revealing behavior in complex cases, and (most crucially): What is evolvable?

3. Theoretical structure: and theoretical tools:

Unitary and apparently very general methodology and theoretical structure utility calculus [interval]Probability calculus [ratio] optimizationMethods

Piecemeal methodology and theoreticalstructure theories ofadaptive control,learning, behavioralecology, biologicaland cognitive development &evolution.

Rational decision theory tries to get all from utility calculus, placing strong requirements on utilities (and probabilities). Satisficing consistent with theoretical decentralization, local, limited action in non-equilibrium conditions; with decisions as intersection of processes and constraints that are more content and mechanism-specific.


II. COMMONPLACE FEATURES AND THEIR CONSEQUENCES


4. Calculation demands impossibly large relatively small Calculations required for maximization model to generate probabilities not physically computable, compounded by non-linearities, bifurcations, chaos.

5. Knowledge required enormous, global, long-range

small, local, short-range For maximization, total (like LaPlacean demon) because desired states entangled with total states. (less demanding for shorter planning horizons).

6. Optimization global, total (problems arise for multiple optima)

local, incremental With level of aspiration set to “> current value” and a “greedy” algorithm, satisficing yields hill-climbing algorithm. [simple selection on fitness topographies]

7. Alternatives given simultaneously, en bloc

normally sequentially Assuming alternatives are a closed set (specifiable at time of decision) is formative for the maximizing perspective.

8. Computational equilibrium

yes no; computational relaxation time >> decision time unless decisions force satisficing.

Relation between selection mechanisms and relaxation times at different levels of decision processes parallels evolution with multiple units of selection.


III. UTILITY OR RATIONALITY ASSUMPTIONS


9. transitive preferences(no multi-dimensionalchoice or utilities(MDU’s)

required to avoid irrationality (money pump argument)

not required; can path- dependence orcycles (Gilpin 1975)

Intransitivity maymulti-dimensional choice (MDU’s) w.context-sensitive relevance of dimensions. Specifystate may à deterministic outcomes (e.g. Kachalnik)

10. utilities measurable on interval metric

required for decisionunder uncertainty

Ordinal metric or lexicalordering often sufficient

A strong metric is required for maximization view sothat expected utility is well-defined

11. exclusive definition of alternatives

yes (req’d. for def.of expected utility)

No Problems with this clause rarely discussed, but nottractable in the real world. Alternatives are mutuallyexclusively specifiable only in logical space

12. exhaustive definition of alternatives

yes (req’d. for def.of expected utility)

No Needs closure (no exceptions) or wastebasket clause.Exceptionless laws rarely available. WBC open-textured with ill-defined probabilities and utilities.


IV. CONSEQUENCES AND EXTRAPOLATIONS


13. path dependence no: regarded as “irrational”

yes (history matters)

Phylogenetically: inherited mechanismsOntogenetically: development, learning history Socially: prior enculturation of individual.

14. stable preferences assumed (Becker1976)

yes (especiallyuseful for revealedpreference)

not required; mustbe relaxed forFestinger data

Festinger (1957) finds increases in preferencefor chosen alternative after choice; possible mechanism for adaptive “lock in” of choice, no vacillation (WW).

15. dyadic preferences or interactions sufficient to generate phenomena

Archimedean Axiom for utilitiesà pair wisecomparisonsSuffice

MDU’s, lexicalorderings à denialof AA; Chase’sparadox requirestriadic

Convergences through learning from random towards transitivity not possible thru dyadic interactions (Landau); Chase (1974) demonstrates that triadic interactions suffice.

V. TEMPORAL AND SPATIAL STRUCTURE

Feature Maximization SATISFICING Comments

16. temporal structure atemporal or simultaneous, staged, static

temporal, sequential,Dynamic

Rational decision theory framework supposes we canstop to gather information, or it is already given, withonly predictable new options arising during decision.

17. acquire information during decision

no? Yes Info learned is used for the adaptive modification of theaspiration level to converge on an outcome (feedbackis essential).

18. modify decision rules thru experience

No Yes If the process is repeated sufficiently, higher ordersatisficing modification of higher order rules ispossible. Regress is on demand; not vicious.

19. decision structure and process are located

Internal to agent;market asaggregate, socialstructure ignored.

Embodied socializedagent; decisionstructure significantlyexternal.

See Simon (1954) Administrative Behavior,McClamrock 1994, Hutchins 1995, Clark 1997.

20. tools for dealing with temporal patterns of change and adaptation

Cost-benefit and risk-benefit toolsplanning horizon

Threshold-baseddecision rules; relaxation-timedynamics

Heuristics to operationalize risk: Half-life, LD-50, n-year flood plain, wind- and shock-magnitude scales



VI. OTHER TOOLS:

Feature Maximization SATISFICING Comments

21. Heuristics relevant

no yes; extensions ofsatisficing strategy;H’s specialized tospecific classes ofProblems

All biological adaptations meet 6 primarycharacteristics of heuristics (Wimsatt 2006),connecting evolutionary and problem-solvingcontexts.

22. context sensitive rules are relevant

if so, only as partof subject matterof particularDecisions

yes; may be part ofdecision procedureas well as content

Context sensitivities indicate conditions ofapplicability or of successful operation ofheuristics.


• The MPD Program’s Cohort 9 was given the opportunity twice to practice the art of satisficing.

1.) The January Experience: LIGHT-BOT

2.) King Texere - Trebuchet


• The January Experience project required the team to build a robot.

• The basic given objectives were to sort large and small marbles and raise them. Points were given for the # sorted properly and the height raised.

• Bonus points were awarded to the lightest weight for the robot

January Experience Cohort 9

LIGHT – BOTby

BBCBrad Kenoyer

Bill ParranChintan Ved

Product Description

CHASSIS

• Light Weight: Styrofoam packaging mat’l

– Aluminum bracket to support: Lift / Raising

• Support total system design– Drive– Sorter– Lifting (Telescoping)– Raising sorter

DRIVE• (2) Servo drive motors

• (2) Model airplane wheels w/ rubberbands

• (2) Skid buttons

RAISING• (1) Servo motor

• (1) Aluminum Arm

SORTER• “Dust Pan” design

• Collect small and large marbles

• Sort small and large marbles

LIFTING• (1) Servo Motor

• (2) Cardboard Tubes

• (1) Aluminum ‘L’ bracket w/ plastic cap

• (1) Aluminum strip w/ string

Team BBC w/ LIGHT-BOT


• The LIGHT-BOT performed the objectives in the test trials: sorting and raising the marbles and achieved the bonus points for the lightest design

• The LIGHT-BOT failed to perform during the competition.

• Besides some controller issues - due to the ultra-light weight, the servo drives were able to drift during the competition.


Lessons Learned from January Experience:1. Meeting all the individual component objectives

does not necessarily meet the systems requirements.

2. Make sure the components deliver what they were designed for (no compromise once you do all the tradeoffs).

3. Testing system for robustness is a good idea.4. Always have the controller antenna fully

extended.


• King Trexere – Trebuchet Project

• The objective for the Trebchet Project was to follow the SE Design Vee process to produce a working Trebuchet and use in competition to defeat your opponent’s fortress.

• Bonus: Shoot unopposed if weight is less than opponants by ½ lb.


• Team BBCD following the process for the Design Vee, past experience from the January Experience, and utilizing Satisficing: The selection of an acceptable or satisfactory solution that meet an agents minimum aspiration-level or threshold, produced a SATISFICING Trebuchet.

Systems Engineering Vee

Trebuchet Sub-System Verification

Trebuchet Verification

• Propels projectile 15-20’ – YES

• Provides enough force to knock down a pop can – YES

• Weighs less than 5lbs (and competitor’s trebuchet) – YES

• Robust enough for 50+ consistent shots – YES

• All components geometrically compatible -YES

Trebuchet Verification

• Controlled experiments run to optimize firing pin angle, sling length, and projectile location

• Distance, repeatability, and launch angle all considered as variables were finalized

• Fired inside and outside to measure variability to temperature and wind

Final Preparation

• Team firing rolls determined

• Aiming procedure practiced and refined


• Trebuchet Results

• By following the SE Design Vee process and SATISFICING the project (customer) requirements, Team BBCD showed that satisficing works when the design objectives: Understand-specify-design-build-verify will lead to Validation: a light weight, accurate shooting Trebuchet

The Golf ball was shot into the golf ball sleeve from 20’ for verification.

Heuristics

• Make sure you don’t loose sight of the goal or deliverables.

• There is a fine line between must have & nice to have.

• True Validation is SATISFICING the Customer.

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Thank You !!!

Team BBCD…

mpd 575 design for satisficing (dfs) team bbcd cohort #9 b rad kenoyer b ill parran c hintan ved d...

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