In­tro­duc­ing Model-based Prob­lem Solv­ing


Rotman Management Magazine - - FROM THE EDITOR - By Mihnea Moldoveanu and Peter Pauly

Model-based prob­lem solv­ing is a dis­ci­pline nur­tured by decades of sci­en­tific think­ing and prac­tice that is es­sen­tial to the mod­ern solver of busi­ness prob­lems.

what they do in generic terms, many exWHEN ASKED TO DE­SCRIBE ec­u­tives or en­trepreneurs will likely say that they ‘solve prob­lems’. Prob­lem solv­ing is what brings ‘busy­ness’ to busi­ness. How­ever, this phrase mis­leads the mind, be­cause busi­ness prob­lems are not the clean, tidy, well-de­fined puzzles stu­dents ev­ery­where grow up with on ex­ams and prob­lem sets. Prob­lems like: • Econ­o­mists cal­cu­lat­ing the profit-max­i­miz­ing price and

quan­tity con­di­tional on mar­ket con­di­tions; • An engi­neer cal­cu­lat­ing the tran­sient re­sponse of an elec­tri

cal cir­cuit to a sud­den surge in in­put cur­rent; or • A com­pany’s oper­a­tions man­agers iden­ti­fy­ing op­ti­mal de

livery routes.

Though they look quite dif­fer­ent, these prob­lems have some com­mon char­ac­ter­is­tics.

You know the start­ing point and you THEY ARE WELL DE­FINED. have a conception of what the so­lu­tion might look like. You do not need a map or other rep­re­sen­ta­tion to make sense of what the prob­lem is say­ing.

The method by which you atTHEY ARE WELL STRUC­TURED. tempt to solve them will not al­ter the prob­lem it­self, and that method lays out a se­quence of steps (it­er­ated dom­i­nance rea­son­ing, lin­ear pro­gram­ming, or some other stan­dard tool of the dis­ci­pline) that will take you from the prob­lem to a so­lu­tion, which is usu­ally unique.

They are THEY ARE USU­ALLY PER­FORMED BY A SIN­GLE MIND. meant to test in­di­vid­ual prow­ess — ef­fort times abil­ity — and are there­fore solv­able by a sin­gle per­son work­ing alone.

Busi­ness prob­lems, on the other hand, should not even be called ‘prob­lems’ be­cause they are so dif­fer­ent from what we are used to when we hear the word. They should in­stead be called sit­u­a­tions, predica­ments or chal­lenges — and they look more like this:

• De­sign, fi­nance, test build and de­ploy an elec­tric car pro­vi­sion­ing-and-charg­ing in­fra­struc­ture for the province of On­tario within three years; • In­crease the pro­duc­tiv­ity of a 1000-per­son re­search and de­vel­op­ment group in an $8 bil­lion per year gross rev­enue phar­ma­ceu­ti­cals com­pany by 20 per cent over the next six quar­ters; or • Ad­dress what the CEO de­scribes as ‘a sud­den break­down of ac­count­abil­ity’ at the level of the top man­age­ment team of a 70,000-per­son or­ga­ni­za­tion.

These are the types of prob­lems that our ex­ec­u­tives and en­trepreneurs are be­ing called upon to solve. These are the prob­lems of busi­ness, and what makes them so dif­fer­ent from run-of-themill prob­lems is that they are:

You need to come up with mea­sures for suc­cessILL-DE­FINED. ful so­lu­tions and with the vari­ables that you will fo­cus on. For in­stance, pro­duc­tiv­ity in an R&D group can be al­ter­nately de­fined as ‘patents granted per dol­lar spent, ‘num­ber of new prod­ucts in­cor­po­rat­ing in­no­va­tions emerg­ing from the group per head­count’, ‘to­tal gross or net rev­enue gen­er­ated by prod­ucts and ser­vices in­cor­po­rat­ing the group’s in­no­va­tions’ — and so on. And ac­count­abil­ity can be thought of in terms of ‘op­ti­mal align­ment of the ob­jec­tives of the in­di­vid­u­als on the team with those of the busi­ness’ or as ‘the op­ti­miza­tion of the re­li­able flow of ac­cu­rate and timely in­for­ma­tion to those that need it most’, and so on.

The very process of get­ting to­gether a synILL-STRUC­TURED. di­cate to fi­nance new in­fra­struc­ture can trig­ger po­lit­i­cal and eco­nomic ac­tions aimed at de­rail­ing the pro­ject; or, the very act of mak­ing in­quiries into the way in which ex­ec­u­tives pass, dis­tort or with­hold sen­si­tive in­for­ma­tion from one another can cause them to strate­gi­cally ap­pear more open and trust­wor­thy than they nor­mally are;

None THEY RE­QUIRE COL­LAB­O­RA­TION AND COL­LEC­TIVE AC­TION. of these prob­lems can be re­solved by a sin­gle mind work­ing in a cu­bi­cle, of­fice or garage — and as a re­sult, re­solv­ing the ten­sions, clashes and sub­ver­sions that come to light when hu­mans work to­gether be­comes part of ev­ery prob­lem you are try­ing to solve.

To de­fine such prob­lems, you need mod­els or ‘maps’ of the situ- ation that rep­re­sent what you see and sense onto what you can think with — like vari­ables and the re­la­tions be­tween them. To struc­ture these prob­lems, you need to model not only the sit­u­a­tion it­self, but also the way in which you are go­ing to solve it. And to col­lab­o­rate pro­duc­tively, you need to be able to com­mu­ni­cate, re­late and co-rea­son with oth­ers through the ob­jec­tives, data and mod­els you use to make sense of the sit­u­a­tion.

These ‘maps’ can be pre­cise quan­ti­ta­tive state­ments of a set of re­la­tion­ships, or they can be men­tal maps of the in­ter­ac­tions be­tween ac­tors and/or ob­jects. The bot­tom line is, to ar­rive at repli­ca­ble and ac­tion­able so­lu­tions, we need struc­tural think­ing. In short, we need Model-based Prob­lem Solv­ing.

The El­e­ments of Model-based Prob­lem Solv­ing

Model-based Prob­lem Solv­ing is the dis­ci­pline of defin­ing and struc­tur­ing a sit­u­a­tion or predica­ment, us­ing the for­mal and in­for­mal lan­guage sys­tems that re­searchers and sci­en­tists have de­vel­oped. It is a dis­tinc­tive ap­proach to busi­ness prob­lem solv­ing that com­bines the rigor of for­mal (an­a­lyt­i­cal or men­tal) mod­el­ing with the rel­e­vance and ac­tion­abil­ity of ap­plied prob­lem solv­ing. Like all dis­ci­plines, it has sev­eral com­po­nents.

To make sense of a predica­ment, you need to MODEL BUILD­ING. map it out. Just as a map is a sim­pli­fi­ca­tion of a ge­o­graph­i­cal do­main, a model is a sim­pli­fi­ca­tion of a sit­u­a­tion: it cap­tures the vari­ables that mat­ter and the re­la­tion­ships be­tween them.

Mod­els from dif­fer­ent dis­ci­plines can be used to make sense of a predica­ment and will yield dif­fer­ent prob­lem state­ments. In terms of un­der­stand­ing the role of in­ter­per­sonal re­la­tions, take our ‘ac­count­abil­ity cri­sis’ ex­am­ple. We could use net­work mod­els to rep­re­sent the in­for­ma­tion flows be­tween ex­ec­u­tives — via for­mal and in­for­mal ties and in­ter­ac­tions — and then seek to op­ti­mize the speed, re­li­a­bil­ity and ac­cu­racy of those flows to in­crease the de­gree to which ev­ery­one is in­formed of ev­ery prom­ise made, kept or bro­ken at the top man­age­ment level.

Or, we could use mod­els of eco­nomic agency to rep­re­sent the au­thor­ity (i.e. de­ci­sion rights) and the in­cen­tives of each of the ex­ec­u­tives in­volved, and then seek to re-al­lo­cate de­ci­sion rights and in­cen­tives so as to max­i­mize the de­gree to which each per­son has the right to make the de­ci­sions she is best equipped to make, and the de­gree to which her in­cen­tives are aligned with those of the or­ga­ni­za­tion as a whole.

Al­ter­na­tively, we could model the process by which the team makes de­ci­sions as ‘a set of causal in­ter­ac­tions be­tween the brain states of ex­ec­u­tive team mem­bers’, and seek to op­ti­mize the

emo­tional and vis­ceral land­scape of meet­ings by chang­ing their length, for­mat, and phys­i­o­log­i­cal cost (by pay­ing at­ten­tion to the ef­fects of low blood sugar, for in­stance) for each team mem­ber.

Just as you test a map by us­ing it to nav­i­gate a MODEL TEST­ING. known ter­rain be­fore you ven­ture off into un­known hin­ter­lands, you test mod­els by spec­i­fy­ing the re­la­tion­ships you would ex­pect to hold if the model were true and test­ing them against data you al­ready have.

That is most easily un­der­stood in the con­text of try­ing to iden­tify the causal re­la­tions be­tween vari­ables in one’s map — say, prices and quan­ti­ties. But we can also ex­plore the role of or­ga­ni­za­tional struc­tures, such as ‘the re­la­tion­ship be­tween the de­cen­tral­iza­tion of de­ci­sion rights and top man­age­ment team per­for­mance’ by test­ing the re­la­tion­ship be­tween per­for­mance and de­ci­sion-right cen­tral­iza­tion in dif­fer­ent or­ga­ni­za­tions in the same in­dus­try, or in dif­fer­ent in­dus­tries; or, by look­ing at changes in per­for­mance in­duced by changes in de­ci­sion-right al­lo­ca­tions in top man­age­ment de­ci­sion pro­cesses in the same firm, or across sev­eral firms where such changes can be doc­u­mented.

Like­wise, we could test mod­els of team func­tion­ing based on phys­i­o­log­i­cal op­ti­miza­tion of the mi­lieu in which de­ci­sions are made (‘meet­ings’) by ex­am­in­ing the per­for­mance of var­i­ous teams us­ing dif­fer­ent de­ci­sion-mak­ing pro­to­cols un­der dif­fer­ent phys­i­o­log­i­cal con­di­tions; or by look­ing at changes in the per­for­mance of a sin­gle team af­ter mak­ing changes to the way they in­ter­act that im­pact the neu­ro­phys­i­o­log­i­cal con­di­tions of each team mem­ber.

Just as maps have free pa­ram­e­ters (like scale MODEL CAL­I­BRA­TION. and tex­ture sym­bols) that must be in­ter­preted and adapted to the spe­cific land­scape you are nav­i­gat­ing, mod­els have free pa­ram­e­ters that must be tai­lored to the spe­cific sit­u­a­tion you are at­tempt­ing to act upon. Where struc­tural re­la­tions can be quan­ti­fied and his­tor­i­cal data are avail­able, a range of data-driven tools are avail­able for struc­tural iden­ti­fi­ca­tion. But we can do more to un­der­stand the in­ter­per­sonal di­men­sions of a busi­ness prob­lem.

Take our re­search and de­vel­op­ment pro­duc­tiv­ity chal­lenge. Fo­cus on a spe­cific devel­oper (‘Tom’) and sup­pose that we have built a set of mod­els for un­der­stand­ing the vari­ables that make a dif­fer­ence to his pro­duc­tiv­ity, and tested these mod­els to re­cover a set of re­la­tion­ships we be­lieve to be valid for peo­ple in sim­i­lar po­si­tions. We now have ‘loose maps’ ( Fig­ure One) for how Tom’s ef­fort level re­sponds to in­cen­tives (i.e. how ‘mo­ti­vat­able’ he is); how his out­put varies with his ef­fort (i.e. how com- pe­tent he is); how his mo­ti­va­tion varies with pay­offs to other de­vel­op­ers (i.e. how en­vi­ous he is); how likely he is to tell the truth when he is per­versely in­cen­tivized to dis­tort or hide it — and so forth.

We now need to cal­i­brate the gen­eral form of these re­la­tion­ships to the spe­cific sit­u­a­tion at hand — to the R&D de­vel­op­ers that we are deal­ing with here and now. For this, we have to col­lect data on the spe­cific re­la­tion­ships be­tween in­cen­tives and ef­fort lev­els, ef­fort and out­put, per­verse in­cen­tives and prob­a­bil­ity of dis­tor­tion that will de­scribe Tom’s be­hav­iour — and that of other key play­ers in the R&D group.

As in the case of quan­ti­ta­tive model build­ing and test­ing, the ap­plied busi­ness prob­lem solver can ben­e­fit greatly from the ex­per­i­men­tal art of the so­cial sci­en­tist, who must de­sign both un­ob­tru­sive mea­sures for the vari­ables she is in­ter­ested in, and valid tests that will help her ‘tighten’ her map of the do­main.

When we work on busi­ness prob­lems, we are in­MODEL FU­SION. ter­ested in pre­dict­ing and act­ing as well as in repli­ca­bil­ity. As a re­sult, we may wish to com­bine pre­dic­tions and fore­casts from dif­fer­ent mod­els — just as we would com­bine topo­graph­i­cal, mor­pho­log­i­cal and ge­o­log­i­cal maps in or­der to re­con­struct—as re­al­is­ti­cally as pos­si­ble — the likely pit­falls, wa­ter­falls and land­falls of a mapped ter­rain.

Ev­ery map has a ‘do­main of rel­e­vance’ and a ‘ra­dius of va­lid­ity’ — and so does ev­ery model. We can have, for in­stance, dif­fer­ent mod­els of ‘the ef­fects of prices on sales vol­ume’ or ‘the ef­fi­cacy of in­cen­tives on man­age­rial in­tegrity’, that have been tested in dif­fer­ent data sets in dif­fer­ent in­dus­tries, and there­fore have very dif­fer­ent func­tional forms. Should we av­er­age across them to come up with an ‘uber-model’? How do we re­solve the al­most in­evitable model clash? That de­pends on the spe­cific sit­u­a­tions in which the mod­els were tested: how sim­i­lar or dif­fer­ent they were; how much of a dif­fer­ence to the de­pen­dent mea­sures these dif­fer­ences make; and, in the case of mod­els in­volv­ing choices hu­mans make, the range of op­tions they had at their dis­posal in each sit­u­a­tion in which the model was tested. Pre­ci­sion in all things—in mat­ters of model spec­i­fi­ca­tion and test­ing — is para­mount in the del­i­cate game of model fu­sion.

Alas, fus­ing mod­els to gen­er­ate bet­ter pre­dic­tions — even if car­ried out with care and pre­ci­sion — works most re­li­ably only when the mod­els use the same ba­sic lan­guage. For in­stance, when they are all eco­nomic mod­els, or phys­i­o­log­i­cal mod­els, or func­tion mod­els of net­work func­tion and per­for­mance.

How­ever, science — as well as meta­physics and ‘lay think­ing’— turns up a panoply of mod­els that are of­ten vastly dif­fer­ent. Some are re­la­tional — like eco­nomic mod­els of mar­kets; some are struc­tural, like ge­o­log­i­cal mod­els; some are causal, like phys­i­o­log­i­cal or psy­cho­log­i­cal mod­els of be­hav­iour; some are tele­o­log­i­cal, like eco­nomic mod­els of choice; and some — like so­ci­o­log­i­cal mod­els of class war­fare and ide­o­log­i­cal con­flict — are func­tional, in that they ex­plain phe­nom­ena in terms of func­tions.

When mod­els are de­rived from dif­fer­ent lan­guage sys­tems, fu­sion of­ten fal­ters; in which case we need some­thing that goes be­yond it — some­thing that in­te­grates rather than just ag­gre­gates.

A Foun­da­tion for In­te­gra­tive Rea­son­ing

Busi­ness prob­lem solv­ing of­ten in­volves the syn­the­siza­tion of past ex­pe­ri­ence to­wards a new (and bet­ter) so­lu­tion. Gen­uine in­no­va­tions, how­ever — which ex­pand the realm of pos­si­bil­i­ties — are based on cre­ativ­ity and in­ge­nu­ity di­rected to­wards the de­sign of a new prod­uct, process or ex­pe­ri­ence.

fa­mously char­ac­ter­ized the ‘de­signer of so­lu­tions’ as some­one who ‘de­vises cour­ses of ac­tion to change ex­ist­ing sit­u­a­tions into pre­ferred ones’. The act of gen­er­at­ing op­tions in­volves em­pa­thetic lis­ten­ing, the play­ful gen­er­a­tion of ideas (e.g. pro­to­typ­ing) and a ju­di­cious in­ter­weav­ing of

Herbert Si­mon

anal­y­sis and in­tu­ition. But while the gen­er­a­tion of ideas may well be un­struc­tured, their im­ple­men­ta­tion and re­al­iza­tion re­quires a (con­scious or un­con­scious) map.

Fur­ther­more, busi­ness de­ci­sions do not oc­cur in the vac­uum of one’s mind, but in the cal­dron of the board­room and the of­fice. It is a process fraught with po­lit­i­cal and in­ter­per­sonal con­flict and ten­sion, but which re­lies on col­lab­o­ra­tion and co­op­er­a­tion in spite of the emo­tional land­scape on which it un­folds. No sin­gle mind can be­hold the right so­lu­tion at a glance, nor can any sin­gle mind even be­hold all of the promis­ing so­lu­tions. As a re­sult, dis­agree­ment needs to be har­nessed rather than cam­ou­flaged in or­der to un­cover all of the sources of value that each con­trib­u­tor brings to the ta­ble. This of­ten re­quires the de­ploy­ment of spe­cial skills and per­sonal ‘tech­nolo­gies’ to turn dis­agree­ment into pro­duc­tive and gen­er­a­tive ten­sion.

These are pow­er­ful in­stru­ments, but they rely on a level of speci­ficity about what we dis­agree on — and a level of agree­ment on what we do agree on. Such ses­sions are more likely de­scribed by com­bi­na­tions of dis­agree­ments on what we want and how the world works that go hand in hand with one another. For ex­am­ple, • One may have a nor­ma­tive, ide­o­log­i­cal com­mit­ment to egal­i­tar­ian sys­tems, cou­pled with a base of facts and mod­els that show egal­i­tar­ian com­pen­sa­tion sys­tems ex­hibit bet­ter per­for­mance over time; and face • One who has an equally pow­er­ful lib­er­tar­ian com­mit­ment to in­di­vid­u­al­is­tic val­ues and rad­i­cal re­spon­si­bil­ity, cou­pled with a fact base that shows how mak­ing com­pen­sa­tion vari­able and de­pen­dent on in­di­vid­ual out­put, mak­ing in­di­vid-

Dis­agree­ment — as Howard Steven­son has pointed out — can be un­der­stood as hav­ing two com­po­nents: dis­agree­ments about what we want — i.e. di­ver­gences in our goals and ob­jec­tives; and dis­agree­ments about how the world works — i.e. di­ver­gences in the facts, data, mod­els and in­fer­ences we draw from them. Over time, hu­mans have evolved highly spe­cial­ized and con­flict-spe­cific tools and tech­niques for re­solv­ing ‘pure’ types of dis­agree­ment: • Model val­i­da­tion, fu­sion and se­lec­tion tech­niques to help us fig­ure out which mod­els are valid in view of what we per­ceive to be true, to re­solve (within the lim­its of un­cer­tainty) dis­agree­ments about how the world works. • Multi-ob­jec­tive op­ti­miza­tion, co­op­er­a­tive and com­pet­i­tive bar­gain­ing, and com­pet­i­tive game the­ory to help us re­solve — or at least think our ways to the best out­come of — dis­agree­ments about what we want. ual out­put mea­sur­able, and smooth­ing out kinks in a pay for per­for­mance sys­tem in­creases the per­for­mance of the or­ga­ni­za­tions in one’s sam­ple.

The nor­ma­tive (what we should do) and the de­scrip­tive ( what is) go hand in hand in these cases, and what we see is only the tip of the ice­berg — the pre­scrip­tive (or, what we should do given what we think is): ‘Re­ward in­di­vid­ual ef­fort with­out re­gard to av­er­age com­pen­sa­tion’; or ‘Make in­di­vid­ual com­pen­sa­tion re­spon­sive to the av­er­age com­pen­sa­tion of the team’ — which hides all of the nor­ma­tive and de­scrip­tive com­po­nents of each per­son’s ‘model of the world’ and brings the dis­agree­ment to the boil of a real con­flict.

This ‘pre­scrip­tive di­men­sion’ is the bat­tle­ground for busi­ness prob­lem solvers ev­ery­where. While sci­en­tists of­ten use mod­els to re­fer to di­a­grams of cause-and-ef­fect among vari­ables — which they take great pains to de­fine in ob­jec­tively mea­sur­able ways — the mind of the busi­ness per­son most of­ten hears ‘mod­els’ to be short for ‘busi­ness mod­els’ — which are heav­ily pre­scrip­tive in na­ture and in­di­cate, ‘Here is what we should do to ap­pro­pri­ate value, given what we cur­rently know.’

Be­cause busi­ness mod­els are pre­scrip­tive and are stated as state­ments about what one should do, their as­sump­tions must be laid bare be­fore we can use any of our so­phis­ti­cated in­stru­ments for re­solv­ing dis­agree­ment to bring forth bet­ter al­ter­na­tives. To do this work, we need another fam­ily of tools. Hence, the fi­nal re­quire­ment for solv­ing busi­ness prob­lems in col­lab­o­ra­tive

set­tings is a set of tools that turn dis­agree­ment into gen­er­a­tive ten­sion. How? Rec­og­niz­ing that dis­agree­ment has sep­a­rate-but­cou­pled com­po­nents (nor­ma­tive and de­scrip­tive), it seeks to first ‘un­pack’ any pre­scrip­tions — in­clud­ing busi­ness mod­els — into their nor­ma­tive and de­scrip­tive com­po­nents. • Nor­ma­tive: ‘What do we want to hap­pen or be­lieve should

hap­pen?’; and • De­scrip­tive: ‘What do we think or be­lieve to be true?’ Af­ter val­i­dat­ing the de­scrip­tive com­po­nents of each pre­scrip­tive model (‘what would be true if this belief were valid?’) it pro­ceeds to search for a bet­ter model by se­lec­tively re­com­bin­ing the de­scrip­tive and pre­scrip­tive com­po­nents of each of the pre­scrip­tive mod­els that ‘clash’.

If, for ex­am­ple: Pre­scrip­tive Model A is, ‘in­crease ac­count­abil­ity by op­ti­miz­ing the phys­i­o­log­i­cal mi­lieu of ex­ec­u­tive meet­ings’ (and re­lies on nor­ma­tive as­sump­tions about the phys­i­o­log­i­cal de­ter­min­ism of ac­tion — how to ma­nip­u­late brains to get bod­ies to act); and Pre­scrip­tive Model B is, ‘in­crease ac­count­abil­ity by in­still­ing a cul­ture of rad­i­cal re­spon­si­bil­ity and vest­ing de­ci­sion rights in those who have the most ac­cu­rate in­for­ma­tion and the most rel­e­vant knowl­edge’ (and re­lies on ‘tested’ mod­els of con­sumer and strate­gic be­hav­iour, and a nor­ma­tive com­mit­ment to in­di­vid­ual re­spon­si­bil­ity), then – The in­te­gra­tive mind­set will - un­cover and sep­a­rate out the nor­ma­tive and de­scrip­tive

com­po­nents of each model; - seek to re­tain only those de­scrip­tive com­po­nents that

have been val­i­dated; and then - se­lec­tively re­com­bine de­scrip­tive and nor­ma­tive com­po­nents from each model to get to a so­lu­tion that will be nei­ther op­ti­mal nor ideal, but bet­ter than the ‘pure’ al­ter­na­tives.

Such a so­lu­tion most of­ten emerges as an an­swer to ques­tions raised by the pro­duc­tive ten­sion that was gen­er­ated by the clash of the mod­els, such as: • What are the ar­eas and do­mains in which ra­tio­nal­ity fails and

fal­ters — and we need to fo­cus on minds rather than brains? • What are the means by which the self-con­trol and self-com­mand of ex­ec­u­tives can be bol­stered in ways that in­crease their abil­ity to make and de­liver on prom­ises? • What are the op­ti­mal con­di­tions for meet­ings and gath­er­ings that make ex­ec­u­tive team mem­bers heed rea­sons and ar­gu­ments, as op­posed to vis­ceral sen­sa­tions and hot emo­tions? At­tend­ing to the above will serve to gen­er­ate mod­els of in­ter­ac­tion and ex­ec­u­tive team de­vel­op­ment that in­crease ac­count­abil­ity writ large.

In clos­ing

Model-based Prob­lem Solv­ing is a dis­ci­pline nur­tured by decades of sci­en­tific think­ing and prac­tice which is es­sen­tial to the mod­ern solver of busi­ness prob­lems. As in­di­cated herein, it pro­vides a foun­da­tion for a com­pre­hen­sive and in­te­gra­tive ap­proach to defin­ing and struc­tur­ing busi­ness prob­lems: the dis­ci­pline to base de­ci­sions on repli­ca­ble and (when­ever pos­si­ble) val­i­dated struc­tural ra­tio­nales, the fa­cil­ity to trans­late in­no­va­tive and in­trin­si­cally non-testable ideas into re­al­ity, and a sys­tem­atic way to re­solve in­ter­per­sonal ten­sions and op­pos­ing, dis­sent­ing views.

It is in­deed a foun­da­tion for the broader craft of an­a­lyz­ing and re­solv­ing predica­ments of any kind, which are bound to turn up wher­ever big chal­lenges are tack­led. Like all dis­ci­plines, Model-based Prob­lem Solv­ing is trans­ferrable through prac­tice and feed­back, and not just teach­ing and ‘im­print­ing’; and, like most dis­ci­plines, it builds skill through rep­e­ti­tion and con­stant adap­ta­tion.




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