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Ment%l Models: The Best W%y to M%ke Intelligent
Decisions (109 Models Expl%ined)
We wrote ( book on ment(l models. You c(n pre-order (n (udible or kindle copy. Ment(l models (re how we underst(nd the world. Not only do they sh(pe wh(t we think (nd how we underst(nd but they sh(pe the connections (nd opportunities th(t we see. Ment(l models (re how we simplify complexity, why we consider some things more relev(nt th(n others, (nd how we re(son. A ment(l model is simply ( represent(tion of how something works. We c(nnot keep (ll of the det(ils of the world in our br(ins, so we use models to simplify the complex into underst(nd(ble (nd org(niz(ble chunks.
Thinking Better
The qu(lity of our thinking is proportion(l to the models in our he(d (nd their usefulness in the situ(tion (t h(nd. The more models you h(ve—the bigger your toolbox—the more likely you (re to h(ve the right models to see re(lity. It turns out th(t when it comes to improving your (bility to m(ke decisions v(riety m(tters. Most of us, however, (re speci(lists. Inste(d of ( l(tticework of ment(l models, we h(ve ( few from our discipline. E(ch speci(list sees something different. By def(ult, ( typic(l Engineer will think in systems. A psychologist will think in terms of incentives. A biologist will think in terms of evolution. By putting these disciplines together in our he(d, we c(n w(lk (round ( problem in ( three dimension(l w(y. If weʼre only looking (t the problem one w(y, weʼve got ( blind spot. And blind spots c(n kill you. Hereʼs (nother w(y to think (bout it. When ( bot(nist looks (t ( forest they m(y focus on the ecosystem, (n environment(list sees the imp(ct of clim(te ch(nge, ( forestry engineer the st(te of the tree growth, ( business person the v(lue of the
l(nd. None (re wrong, but neither (re (ny of them (ble to describe the full scope of the forest. Sh(ring knowledge, or le(rning the b(sics of the other disciplines, would le(d to ( more well-rounded underst(nding th(t would (llow for better initi(l decisions (bout m(n(ging the forest. In ( f(mous speech in the 1990s, Ch(rlie Munger summed up the (ppro(ch to pr(ctic(l wisdom through underst(nding ment(l models by s(ying: “Well, the first rule is th(t you c(nʼt re(lly know (nything if you just remember isol(ted f(cts (nd try (nd b(ng ʼem b(ck. If the f(cts donʼt h(ng together on ( l(tticework of theory, you donʼt h(ve them in ( us(ble form. Youʼve got to h(ve models in your he(d. And youʼve got to (rr(y your experience both vic(rious (nd direct on this l(tticework of models. You m(y h(ve noticed students who just try to remember (nd pound b(ck wh(t is remembered. Well, they f(il in school (nd in life. Youʼve got to h(ng experience on ( l(tticework of models in your he(d.”
A L%tticework of Ment%l Models
To help you build your l(tticework of ment(l models so you c(n m(ke better decisions, weʼve collected (nd summ(rized the ones weʼve found the most useful. And remember: Building your l(tticework is ( lifelong project. Stick with it, (nd youʼll find th(t your (bility to underst(nd re(lity, m(ke consistently good decisions, (nd help those you love will (lw(ys be improving.
The F%rn%m Street L%tticework of Ment%l Models
Gener%l Thinking Concepts
1. The M%p is not the Territory The m(p of re(lity is not re(lity. Even the best m(ps (re imperfect. Th(tʼs bec(use they (re reductions of wh(t they represent. If ( m(p were to represent the territory with perfect fidelity, it would no longer be ( reduction (nd thus would no longer be useful to us. A m(p c(n (lso be ( sn(pshot of ( point in time, representing something th(t no longer exists. This is import(nt to keep in mind (s we think through problems (nd m(ke better decisions.
soci(l world is s(id to be f(t-t(iled r(ther th(n norm(lly distributed. (nd B(yesi(n Upd(ting The B(yesi(n method is ( method of thought (n(med for Thom(s B(yes) whereby one t(kes into (ccount (ll prior relev(nt prob(bilities (nd then increment(lly upd(tes them (s newer inform(tion (rrives. This method is especi(lly productive given the fund(ment(lly non-deterministic world we experience: We must use prior odds (nd new inform(tion in combin(tion to (rrive (t our best decisions. This is not necess(rily our intuitive decision-m(king engine.
7. Inversion Inversion is ( powerful tool to improve your thinking bec(use it helps you identify (nd remove obst(cles to success. The root of inversion is “invert,” which me(ns to upend or turn upside down. As ( thinking tool it me(ns (ppro(ching ( situ(tion from the opposite end of the n(tur(l st(rting point. Most of us tend to think one w(y (bout ( problem: forw(rd. Inversion (llows us to flip the problem (round (nd think b(ckw(rd. Sometimes itʼs good to st(rt (t the beginning, but it c(n be more useful to st(rt (t the end. 8. Occ%mʼs R%zor Simpler expl(n(tions (re more likely to be true th(n complic(ted ones. This is the essence of Occ(mʼs R(zor, ( cl(ssic principle of logic (nd problem-solving. Inste(d of w(sting your time trying to disprove complex scen(rios, you c(n m(ke decisions more confidently by b(sing them on the expl(n(tion th(t h(s the fewest moving p(rts. Re(d more on Occ(mʼs R(zor 9. H%nlonʼs R%zor H(rd to tr(ce in its origin, H(nlonʼs R(zor st(tes th(t we should not (ttribute to m(lice th(t which is more e(sily expl(ined by stupidity. In ( complex world, using this model helps us (void p(r(noi( (nd ideology. By not gener(lly (ssuming th(t b(d results (re the f(ult of ( b(d (ctor, we look for options inste(d of missing opportunities. This model reminds us th(t people do m(ke mist(kes. It dem(nds th(t we (sk if there is (nother re(son(ble expl(n(tion for the events th(t h(ve occurred. The expl(n(tion most likely to be right is the one th(t cont(ins the le(st (mount of intent.
Numer%cy
1. Permut%tions %nd Combin%tions The m(them(tics of permut(tions (nd combin(tions le(ds us to underst(nd the pr(ctic(l prob(bilities of the world (round us, how things c(n be ordered, (nd how we should think (bout things. 2. Algebr%ic Equiv%lence The introduction of (lgebr( (llowed us to demonstr(te m(them(tic(lly (nd (bstr(ctly th(t two seemingly different things could be the s(me. By m(nipul(ting symbols, we c(n demonstr(te equiv(lence or inequiv(lence, the use of which led hum(nity to untold engineering (nd technic(l (bilities. Knowing (t le(st the b(sics of (lgebr( c(n (llow us to underst(nd ( v(riety of import(nt results.
3. R%ndomness Though the hum(n br(in h(s trouble comprehending it, much of the world is composed of r(ndom, non-sequenti(l, non-ordered events. We (re “fooled” by r(ndom effects when we (ttribute c(us(lity to things th(t (re (ctu(lly outside of our control. If we donʼt course-correct for this fooled-by-r(ndomness effect – our f(ulty sense of p(ttern-seeking – we will tend to see things (s being more predict(ble th(n they (re (nd (ct (ccordingly. 4. Stoch%stic Processes (Poisson, M%rkov, R%ndom W%lk) A stoch(stic process is ( r(ndom st(tistic(l process (nd encomp(sses ( wide v(riety of processes in which the movement of (n individu(l v(ri(ble c(n be impossible to predict but c(n be thought through prob(bilistic(lly. The wide v(riety of stoch(stic methods helps us describe systems of v(ri(bles through prob(bilities without necess(rily being (ble to determine the position of (ny individu(l v(ri(ble over time. For ex(mple, itʼs not possible to predict stock prices on ( d(y-to-d(y b(sis, but we c(n describe the prob(bility of v(rious distributions of their movements over time. Obviously, it is much more likely th(t the stock m(rket (( stoch(stic process) will be up or down 1% in ( d(y th(n up or down 10%, even though we c(nʼt predict wh(t tomorrow will bring. 5. Compounding Itʼs been s(id th(t Einstein c(lled compounding ( wonder of the world. He prob(bly didnʼt, but it is ( wonder. Compounding is the process by which we (dd interest to ( fixed sum, which then e(rns interest on the previous sum !nd the newly (dded interest, (nd then e(rns interest on th(t (mount, (nd so on !d infinitum. It is (n exponenti!l effect, r(ther th(n ( line(r, or (dditive, effect. Money is not the only thing th(t compounds; ide(s (nd rel(tionships do (s well. In t(ngible re(lms, compounding is (lw(ys subject to physic(l limits (nd diminishing returns; int(ngibles c(n compound more freely. Compounding (lso le(ds to the time v(lue of money, which underlies (ll of modern fin(nce. 6. Multiplying by Zero Any re(son(bly educ(ted person knows th(t (ny number multiplied by zero, no m(tter how l(rge the number, is still zero. This is true in hum(n systems (s well (s m(them(tic(l ones. In some systems, ( f(ilure in one (re( c(n neg(te gre(t effort in (ll other (re(s. As simple multiplic(tion would show, fixing the “zero” often h(s ( much gre(ter effect th(n does trying to enl(rge the other (re(s. 7. Churn Insur(nce comp(nies (nd subscription services (re well (w(re of the concept of churn – every ye(r, ( cert(in number of customers (re lost (nd must be repl(ced. St(nding still is the equiv(lent of losing, (s seen in the model c(lled the “Red Queen Effect.” Churn is present in m(ny business (nd hum(n systems: A const(nt figure is periodic(lly lost (nd must be repl(ced before (ny new figures (re (dded over the top. 8. L%w of L%rge Numbers One of the fund(ment(l underlying (ssumptions of prob(bility is th(t (s more
decre(se of increment(l v(lue. A good ex(mple would be ( poor f(mily: Give them enough money to thrive, (nd they (re no longer poor. But (fter ( cert(in point, (ddition(l money will not improve their lot; there is ( cle(r diminishing return of (ddition(l doll(rs (t some roughly qu(ntifi(ble point. Often, the l(w of diminishing returns veers into neg(tive territory – i.e., receiving too much money could destroy the poor f(mily.
3. P%reto Principle N(med for It(li(n polym(th Vilfredo P(reto, who noticed th(t 80% of It(lyʼs l(nd w(s owned by (bout 20% of its popul(tion, the P(reto Principle st(tes th(t ( sm(ll (mount of some phenomenon c(uses ( disproportion(tely l(rge effect. The P(reto Principle is (n ex(mple of ( power-l(w type of st(tistic(l distribution – (s distinguished from ( tr(dition(l bell curve – (nd is demonstr(ted in v(rious phenomen( r(nging from we(lth to city popul(tions to import(nt hum(n h(bits. 4. Feedb%ck Loops (%nd Homeost%sis) All complex systems (re subject to positive (nd neg(tive feedb(ck loops whereby A c(uses B, which in turn influences A ((nd C), (nd so on – with higher-order effects frequently resulting from continu(l movement of the loop. In ( homeost(tic system, ( ch(nge in A is often brought b(ck into line by (n opposite ch(nge in B to m(int(in the b(l(nce of the system, (s with the temper(ture of the hum(n body or the beh(vior of (n org(niz(tion(l culture. Autom(tic feedb(ck loops m(int(in ( “st(tic” environment unless (nd until (n outside force ch(nges the loop. A “run(w(y feedb(ck loop” describes ( situ(tion in which the output of ( re(ction becomes its own c(t(lyst ((uto-c(t(lysis). 5. Ch%os Dyn%mics (Butterfly Effect)/ (Sensitivity to Initi%l Conditions) In ( world such (s ours, governed by ch(os dyn(mics, sm(ll ch(nges (perturb(tions) in initi(l conditions h(ve m(ssive downstre(m effects (s ne(r- infinite feedb(ck loops occur; this phenomenon is (lso c(lled the butterfly effect. This me(ns th(t some (spects of physic(l systems (like the we(ther more th(n ( few d(ys from now) (s well (s soci(l systems (the beh(vior of ( group of hum(n beings over ( long period) (re fund(ment(lly unpredict(ble. 6. Preferenti%l Att%chment (Cumul%tive Adv%nt%ge) A preferenti(l (tt(chment situ(tion occurs when the current le(der is given more of the rew(rd th(n the l(gg(rds, thereby tending to preserve or enh(nce the st(tus of the le(der. A strong network effect is ( good ex(mple of preferenti(l (tt(chment; ( m(rket with 10x more buyers (nd sellers th(n the next l(rgest m(rket will tend to h(ve ( preferenti(l (tt(chment dyn(mic. 7. Emergence Higher-level beh(vior tends to emerge from the inter(ction of lower-order components. The result is frequently not line(r – not ( m(tter of simple (ddition – but r(ther non-line(r, or exponenti(l. An import(nt resulting property of emergent beh(vior is th(t it c(nnot be predicted from simply studying the component p(rts. 8. Irreducibility We find th(t in most systems there (re irreducible qu(ntit(tive properties, such (s
complexity, minimums, time, (nd length. Below the irreducible level, the desired result simply does not occur. One c(nnot get sever(l women pregn(nt to reduce the (mount of time needed to h(ve one child, (nd one c(nnot reduce ( successfully built (utomobile to ( single p(rt. These results (re, to ( defined point, irreducible.
9. Tr%gedy of the Commons A concept introduced by the economist (nd ecologist G(rrett H(rdin, the Tr(gedy of the Commons st(tes th(t in ( system where ( common resource is sh(red, with no individu(l responsible for the wellbeing of the resource, it will tend to be depleted over time. The Tr(gedy is reducible to incentives: Unless people coll(bor(te, e(ch individu(l derives more person(l benefit th(n the cost th(t he or she incurs, (nd therefore depletes the resource for fe(r of missing out. 10. Gresh%mʼs L%w Gresh(mʼs L(w, n(med for the fin(ncier Thom(s Gresh(m, st(tes th(t in ( system of circul(ting currency, forged currency will tend to drive out re(l currency, (s re(l currency is ho(rded (nd forged currency is spent. We see ( simil(r result in hum(n systems, (s with b(d beh(vior driving out good beh(vior in ( crumbling mor(l system, or b(d pr(ctices driving out good pr(ctices in ( crumbling economic system. Gener(lly, regul(tion (nd oversight (re required to prevent results th(t follow Gresh(mʼs L(w. 11. Algorithms While h(rd to precisely define, (n (lgorithm is gener(lly (n (utom(ted set of rules or ( “blueprint” le(ding ( series of steps or (ctions resulting in ( desired outcome, (nd often st(ted in the form of ( series of “If → Then” st(tements. Algorithms (re best known for their use in modern computing, but (re ( fe(ture of biologic(l life (s well. For ex(mple, hum(n DNA cont(ins (n (lgorithm for building ( hum(n being. 12. Fr%gility – Robustness – Antifr%gility Popul(rized by N(ssim T(leb, the sliding sc(le of fr(gility, robustness, (nd (ntifr(gility refers to the responsiveness of ( system to increment(l neg(tive v(ri(bility. A fr(gile system or object is one in which (ddition(l neg(tive v(ri(bility h(s ( disproportion(tely neg(tive imp(ct, (s with ( coffee cup sh(ttering from ( 6-foot f(ll, but receiving no d(m(ge (t (ll (r(ther th(n 1/6th of the d(m(ge) from ( 1-foot f(ll. A robust system or object tends to be neutr(l to the (ddition(l neg(tivity v(ri(bility, (nd of course, (n (ntifr(gile system benefits: If there were ( cup th(t got stronger when dropped from 6 feet th(n when dropped from 1 foot, it would be termed (ntifr(gile. 13. B%ckup Systems/Redund%ncy A critic(l model of the engineering profession is th(t of b(ckup systems. A good engineer never (ssumes the perfect reli(bility of the components of the system. He or she builds in redund(ncy to protect the integrity of the tot(l system. Without the (pplic(tion of this robustness principle, t(ngible (nd int(ngible systems tend to f(il over time.
disproportion(te imp(ct if those (round them follow suit on incre(singly l(rge sc(les.
20. Spring-lo%ding A system is spring-lo(ded if it is coiled in ( cert(in direction, positive or neg(tive. Positively spring-lo(ding systems (nd rel(tionships is import(nt in ( fund(ment(lly unpredict(ble world to help protect us (g(inst neg(tive events. The reverse c(n be very destructive. 21. Complex Ad%ptive Systems A complex (d(ptive system, (s distinguished from ( complex system in gener(l, is one th(t c(n underst(nd itself (nd ch(nge b(sed on th(t underst(nding. Complex (d(ptive systems (re soci(l systems. The difference is best illustr(ted by thinking (bout we(ther prediction contr(sted to stock m(rket prediction. The we(ther will not ch(nge b(sed on (n import(nt forec(sterʼs opinion, but the stock m(rket might. Complex (d(ptive systems (re thus fund(ment(lly not predict(ble.
Physic%l World
1. L%ws of Thermodyn%mics The l(ws of thermodyn(mics describe energy in ( closed system. The l(ws c(nnot be esc(ped (nd underlie the physic(l world. They describe ( world in which useful energy is const(ntly being lost, (nd energy c(nnot be cre(ted or destroyed. Applying their lessons to the soci(l world c(n be ( profit(ble enterprise. 2. Reciprocity If I push on ( w(ll, physics tells me th(t the w(ll pushes b(ck with equiv(lent force. In ( biologic(l system, if one individu(l (cts on (nother, the (ction will tend to be reciproc(ted in kind. And of course, hum(n beings (ct with intense reciprocity demonstr(ted (s well. 3. Velocity Velocity is not equiv(lent to speed; the two (re sometimes confused. Velocity is speed plus vector: how f(st something gets somewhere. An object th(t moves two steps forw(rd (nd then two steps b(ck h(s moved (t ( cert(in speed but shows no velocity. The (ddition of the vector, th(t critic(l distinction, is wh(t we should consider in pr(ctic(l life. 4. Rel%tivity Rel(tivity h(s been used in sever(l contexts in the world of physics, but the import(nt (spect to study is the ide( th(t (n observer c(nnot truly underst(nd ( system of which he himself is ( p(rt. For ex(mple, ( m(n inside (n (irpl(ne does not feel like he is experiencing movement, but (n outside observer c(n see th(t movement is occurring. This form of rel(tivity tends to (ffect soci(l systems in ( simil(r w(y. 5. Activ%tion Energy A fire is not much more th(n ( combin(tion of c(rbon (nd oxygen, but the forests (nd co(l mines of the world (re not combusting (t will bec(use such ( chemic(l re(ction requires the input of ( critic(l level of “(ctiv(tion energy” in order to get ( re(ction st(rted. Two combustible elements (lone (re not enough.
6. C%t%lysts A c(t(lyst either kick-st(rts or m(int(ins ( chemic(l re(ction, but isnʼt itself ( re(ct(nt. The re(ction m(y slow or stop without the (ddition of c(t(lysts. Soci(l systems, of course, t(ke on m(ny simil(r tr(its, (nd we c(n view c(t(lysts in ( simil(r light. 7. Lever%ge Most of the engineering m(rvels of the world h(ve been (ccomplished with (pplied lever(ge. As f(mously st(ted by Archimedes, “Give me ( lever long enough (nd I sh(ll move the world.” With ( sm(ll (mount of input force, we c(n m(ke ( gre(t output force through lever(ge. Underst(nding where we c(n (pply this model to the hum(n world c(n be ( source of gre(t success. 8. Inerti% An object in motion with ( cert(in vector w(nts to continue moving in th(t direction unless (cted upon. This is ( fund(ment(l physic(l principle of motion; however, individu(ls, systems, (nd org(niz(tions displ(y the s(me effect. It (llows them to minimize the use of energy, but c(n c(use them to be destroyed or eroded. 9. Alloying When we combine v(rious elements, we cre(te new subst(nces. This is no gre(t surprise, but wh(t c(n be surprising in the (lloying process is th(t 2+2 c(n equ(l not 4 but 6 – the (lloy c(n be f(r stronger th(n the simple (ddition of the underlying elements would le(d us to believe. This process le(ds us to engineer gre(t physic(l objects, but we underst(nd m(ny int(ngibles in the s(me w(y; ( combin(tion of the right elements in soci(l systems or even individu(ls c(n cre(te ( 2+2=6 effect simil(r to (lloying. 10. Viscosity Viscosity is the “me(sure of how h(rd it is for one l(yer of fluid to slide over (nother l(yer.” If ( liquid is h(rd to move it is more viscous. If it is more viscous there is more resist(nce. Viscosity isnʼt usu(lly (n issue for hum(ns. We h(ve to de(l with gr(vity (nd inerti(, (lthough viscosity is (lw(ys present. But for sm(ll p(rticles, gr(vity (nd inerti( become ( non-issue comp(red to viscosity. We thus le(rn th(t when we ch(nge the sc(le we ch(nge wh(t forces (re relev(nt.
The Biologic%l World
1. Incentives All cre(tures respond to incentives to keep themselves (live. This is the b(sic insight of biology. Const(nt incentives will tend to c(use ( biologic(l entity to h(ve const(nt beh(vior, to (n extent. Hum(ns (re included (nd (re p(rticul(rly gre(t ex(mples of the incentive-driven n(ture of biology; however, hum(ns (re complic(ted in th(t their incentives c(n be hidden or int(ngible. The rule of life is to repe(t wh(t works (nd h(s been rew(rded. 2. Cooper%tion (Including Symbiosis %nd Prisonerʼs Dilemm%) Competition tends to describe most biologic(l systems, but cooper(tion (t v(rious levels is just (s import(nt ( dyn(mic. In f(ct, the cooper(tion of (
(nd (sexu(l.
8. Hier%rchic%l %nd Other Org%nizing Instincts Most complex biologic(l org(nisms h(ve (n inn(te feel for how they should org(nize. While not (ll of them end up in hier(rchic(l structures, m(ny do, especi(lly in the (nim(l kingdom. Hum(n beings like to think they (re outside of this, but they feel the hier(rchic(l instinct (s strongly (s (ny other org(nism. This includes the St(nford Prison Experiment (nd Milgr(m Experiments, which demonstr(ted wh(t hum(ns le(rned pr(ctic(lly m(ny ye(rs before: the hum(n bi(s tow(rds being influenced by (uthority. In ( domin(nce hier(rchy such (s ours, we tend to look to the le(der for guid(nce on beh(vior, especi(lly in situ(tions of stress or uncert(inty. Thus, (uthority figures h(ve ( responsibility to (ct well, whether they like it or not. 9. Self-Preserv%tion Instincts Without ( strong self-preserv(tion instinct in (n org(nismʼs DNA, it would tend to dis(ppe(r over time, thus elimin(ting th(t DNA. While cooper(tion is (nother import(nt model, the self-preserv(tion instinct is strong in (ll org(nisms (nd c(n c(use violent, err(tic, (nd/or destructive beh(vior for those (round them. 10. Simple Physiologic%l Rew%rd-Seeking All org(nisms feel ple(sure (nd p(in from simple chemic(l processes in their bodies which respond predict(bly to the outside world. Rew(rd-seeking is (n effective surviv(l-promoting technique on (ver(ge. However, those s(me ple(sure receptors c(n be co-opted to c(use destructive beh(vior, (s with drug (buse. 11. Ex%pt%tion Introduced by the biologist Steven J(y Gould, (n ex(pt(tion refers to ( tr(it developed for one purpose th(t is l(ter used for (nother purpose. This is one w(y to expl(in the development of complex biologic(l fe(tures like (n eyeb(ll; in ( more primitive form, it m(y h(ve been used for something else. Once it w(s there, (nd once it developed further, 3D sight bec(me possible. 12. Ecosystems An ecosystem describes (ny group of org(nisms coexisting with the n(tur(l world. Most ecosystems show diverse forms of life t(king on different (ppro(ches to surviv(l, with such pressures le(ding to v(rying beh(vior. Soci(l systems c(n be seen in the s(me light (s the physic(l ecosystems (nd m(ny of the s(me conclusions c(n be m(de. 13. Niches Most org(nisms find ( niche: ( method of competing (nd beh(ving for surviv(l. Usu(lly, ( species will select ( niche for which it is best (d(pted. The d(nger (rises when multiple species begin competing for the s(me niche, which c(n c(use (n extinction – there c(n be only so m(ny species doing the s(me thing before limited resources give out. 14. Dunb%rʼs Number The prim(tologist Robin Dunb(r observed through study th(t the number of individu(ls ( prim(te c(n get to know (nd trust closely is rel(ted to the size of its
neocortex. Extr(pol(ting from his study of prim(tes, Dunb(r theorized th(t the Dunb(r number for ( hum(n being is somewhere in the 100–250 r(nge, which is supported by cert(in studies of hum(n beh(vior (nd soci(l networks.
Hum%n N%ture & Judgment
1. Trust Fund(ment(lly, the modern world oper(tes on trust. F(mili(l trust is gener(lly ( given (otherwise weʼd h(ve ( hell of ( time surviving), but we (lso choose to trust chefs, clerks, drivers, f(ctory workers, executives, (nd m(ny others. A trusting system is one th(t tends to work most efficiently; the rew(rds of trust (re extremely high. 2. Bi%s from Incentives Highly responsive to incentives, hum(ns h(ve perh(ps the most v(ried (nd h(rdest to underst(nd set of incentives in the (nim(l kingdom. This c(uses us to distort our thinking when it is in our own interest to do so. A wonderful ex(mple is ( s(lesm(n truly believing th(t his product will improve the lives of its users. Itʼs not merely convenient th(t he sells the product; the f(ct of his selling the product c(uses ( very re(l bi(s in his own thinking. 3. P%vlovi%n Associ%tion Iv(n P(vlov very effectively demonstr(ted th(t (nim(ls c(n respond not just to direct incentives but (lso to (ssoci(ted objects; remember the f(mous dogs s(liv(ting (t the ring of ( bell. Hum(n beings (re much the s(me (nd c(n feel positive (nd neg(tive emotion tow(rds int(ngible objects, with the emotion coming from p(st (ssoci(tions r(ther th(n direct effects. 4. Tendency to Feel Envy & Je%lousy Hum(ns h(ve ( tendency to feel envious of those receiving more th(n they (re, (nd ( desire “get wh(t is theirs” in due course. The tendency tow(rds envy is strong enough to drive otherwise irr(tion(l beh(vior, but is (s old (s hum(nity itself. Any system ignor(nt of envy effects will tend to self-immol(te over time. 5. Tendency to Distort Due to Liking/Loving or Disliking/H%ting B(sed on p(st (ssoci(tion, stereotyping, ideology, genetic influence, or direct experience, hum(ns h(ve ( tendency to distort their thinking in f(vor of people or things th(t they like (nd (g(inst people or things they dislike. This tendency le(ds to overr(ting the things we like (nd underr(ting or bro(dly c(tegorizing things we dislike, often missing cruci(l nu(nces in the process. 6. Deni%l Anyone who h(s been (live long enough re(lizes th(t, (s the s(ying goes, “deni(l is not just ( river in Afric(.” This is powerfully demonstr(ted in situ(tions like w(r or drug (buse, where deni(l h(s powerful destructive effects but (llows for beh(vior(l inerti(. Denying re(lity c(n be ( coping mech(nism, ( surviv(l mech(nism, or ( purposeful t(ctic. 7. Av%il%bility Heuristic One of the most useful findings of modern psychology is wh(t D(niel K(hnem(n c(lls the Av(il(bility Bi(s or Heuristic: We tend to most e(sily rec(ll wh(t is
12. L%ngu%ge Instinct The psychologist Steven Pinker c(lls our DNA-level instinct to le(rn gr(mm(tic(lly constructed l(ngu(ge the L(ngu(ge Instinct. The ide( th(t gr(mm(tic(l l(ngu(ge is not ( simple cultur(l (rtif(ct w(s first popul(rized by the linguist No(m Chomsky. As we s(w with the n(rr(tive instinct, we use these instincts to cre(te sh(red stories, (s well (s to gossip, solve problems, (nd fight, (mong other things. Gr(mm(tic(lly ordered l(ngu(ge theoretic(lly c(rries infinite v(rying me(ning. 13. First-Conclusion Bi%s As Ch(rlie Munger f(mously pointed out, the mind works ( bit like ( sperm (nd egg: the first ide( gets in (nd then the mind shuts. Like m(ny other tendencies, this is prob(bly (n energy-s(ving device. Our tendency to settle on first conclusions le(ds us to (ccept m(ny erroneous results (nd ce(se (sking questions; it c(n be countered with some simple (nd useful ment(l routines. 14. Tendency to Overgener%lize from Sm%ll S%mples Itʼs import(nt for hum(n beings to gener(lize; we need not see every inst(nce to underst(nd the gener(l rule, (nd this works to our (dv(nt(ge. With gener(lizing, however, comes ( subset of errors when we forget (bout the L(w of L(rge Numbers (nd (ct (s if it does not exist. We t(ke ( sm(ll number of inst(nces (nd cre(te ( gener(l c(tegory, even if we h(ve no st(tistic(lly sound b(sis for the conclusion. 15. Rel%tive S%tisf%ction/Misery Tendencies The envy tendency is prob(bly the most obvious m(nifest(tion of the rel(tive s(tisf(ction tendency, but ne(rly (ll studies of hum(n h(ppiness show th(t it is rel(ted to the st(te of the person rel(tive to either their p(st or their peers, not (bsolute. These rel(tive tendencies c(use us gre(t misery or h(ppiness in ( very wide v(riety of objectively different situ(tions (nd m(ke us poor predictors of our own beh(vior (nd feelings. 16. Commitment & Consistency Bi%s As psychologists h(ve frequently (nd f(mously demonstr(ted, hum(ns (re subject to ( bi(s tow(rds keeping their prior commitments (nd st(ying consistent with our prior selves when possible. This tr(it is necess(ry for soci(l cohesion: people who often ch(nge their conclusions (nd h(bits (re often distrusted. Yet our bi(s tow(rds st(ying consistent c(n become, (s one w(g put it, ( “hobgoblin of foolish minds” – when it is combined with the first-conclusion bi(s, we end up l(nding on poor (nswers (nd st(nding p(t in the f(ce of gre(t evidence. 17. Hindsight Bi%s Once we know the outcome, itʼs ne(rly impossible to turn b(ck the clock ment(lly. Our n(rr(tive instinct le(ds us to re(son th(t we knew it (ll (long (wh(tever “it” is), when in f(ct we (re often simply re(soning post-hoc with inform(tion not (v(il(ble to us before the event. The hindsight bi(s expl(ins why itʼs wise to keep ( journ(l of import(nt decisions for (n un(ltered record (nd to re-ex(mine our beliefs when we convince ourselves th(t we knew it (ll (long.
18. Sensitivity to F%irness Justice runs deep in our veins. In (nother illustr(tion of our rel(tive sense of well- being, we (re c(reful (rbiters of wh(t is f(ir. Viol(tions of f(irness c(n be considered grounds for reciproc(l (ction, or (t le(st distrust. Yet f(irness itself seems to be ( moving t(rget. Wh(t is seen (s f(ir (nd just in one time (nd pl(ce m(y not be in (nother. Consider th(t sl(very h(s been seen (s perfectly n(tur(l (nd perfectly unn(tur(l in (ltern(ting ph(ses of hum(n existence. 19. Tendency to Overestim%te Consistency of Beh%vior (Fund%ment%l Attribution Error) We tend to over-(scribe the beh(vior of others to their inn(te tr(its r(ther th(n to situ(tion(l f(ctors, le(ding us to overestim(te how consistent th(t beh(vior will be in the future. In such ( situ(tion, predicting beh(vior seems not very difficult. Of course, in pr(ctice this (ssumption is consistently demonstr(ted to be wrong, (nd we (re consequently surprised when others do not (ct in (ccord(nce with the “inn(te” tr(its weʼve endowed them with. 20. Influence of Stress (Including Bre%king Points) Stress c(uses both ment(l (nd physiologic(l responses (nd tends to (mplify the other bi(ses. Almost (ll hum(n ment(l bi(ses become worse in the f(ce of stress (s the body goes into ( fight-or-flight response, relying purely on instinct without the emergency br(ke of D(niel K(hnem(nʼs “System 2” type of re(soning. Stress c(uses h(sty decisions, immedi(cy, (nd ( f(llb(ck to h(bit, thus giving rise to the elite soldiersʼ motto: “In the thick of b(ttle, you will not rise to the level of your expect(tions, but f(ll to the level of your tr(ining.” 21. Survivorship Bi%s A m(jor problem with historiogr(phy – our interpret(tion of the p(st – is th(t history is f(mously written by the victors. We do not see wh(t N(ssim T(leb c(lls the “silent gr(ve” – the lottery ticket holders who did not win. Thus, we over- (ttribute success to things done by the successful (gent r(ther th(n to r(ndomness or luck, (nd we often le(rn f(lse lessons by exclusively studying victors without seeing (ll of the (ccomp(nying losers who (cted in the s(me w(y but were not lucky enough to succeed. 22. Tendency to W%nt to Do Something (Fight/Flight, Intervention, Demonstr%tion of V%lue, etc.) We might term this Boredom Syndrome: Most hum(ns h(ve the tendency to need to (ct, even when their (ctions (re not needed. We (lso tend to offer solutions even when we do not enough knowledge to solve the problem. 23. F%lsific%tion / Confirm%tion Bi%s Wh(t ( m(n wishes, he (lso believes. Simil(rly, wh(t we believe is wh(t we choose to see. This is commonly referred to (s the confirm(tion bi(s. It is ( deeply ingr(ined ment(l h(bit, both energy-conserving (nd comfort(ble, to look for confirm(tions of long-held wisdom r(ther th(n viol(tions. Yet the scientific process – including hypothesis gener(tion, blind testing when needed, (nd objective st(tistic(l rigor – is designed to root out precisely the opposite, which is
these protections, inform(tion (nd cre(tive workers h(ve no defense (g(inst their work being freely distributed.
7. Double-Entry Bookkeeping One of the m(rvels of modern c(pit(lism h(s been the bookkeeping system introduced in Geno( in the 14th century. The double-entry system requires th(t every entry, such (s income, (lso be entered into (nother corresponding (ccount. Correct double-entry bookkeeping (cts (s ( check on potenti(l (ccounting errors (nd (llows for (ccur(te records (nd thus, more (ccur(te beh(vior by the owner of ( firm. 8. Utility (M%rgin%l, Diminishing, Incre%sing) The usefulness of (ddition(l units of (ny good tends to v(ry with sc(le. M(rgin(l utility (llows us to underst(nd the v(lue of one (ddition(l unit, (nd in most pr(ctic(l (re(s of life, th(t utility diminishes (t some point. On the other h(nd, in some c(ses, (ddition(l units (re subject to ( “critic(l point” where the utility function jumps discretely up or down. As (n ex(mple, giving w(ter to ( thirsty m(n h(s diminishing m(rgin(l utility with e(ch (ddition(l unit, (nd c(n eventu(lly kill him with enough units. 9. Bottlenecks A bottleneck describes the pl(ce (t which ( flow (of ( t(ngible or int(ngible) is stopped, thus holding it b(ck from continuous movement. As with ( clogged (rtery or ( blocked dr(in, ( bottleneck in production of (ny good or service c(n be sm(ll but h(ve ( disproportion(te imp(ct if it is in the critic(l p(th. 10. Bribery Often ignored in m(instre(m economics, the concept of bribery is centr(l to hum(n systems: Given the ch(nce, it is often e(sier to p(y ( cert(in (gent to look the other w(y th(n to follow the rules. The enforcer of the rules is then neutr(lized. This principle/(gent problem c(n be seen (s ( form of (rbitr(ge. 11. Arbitr%ge Given two m(rkets selling (n identic(l good, (n (rbitr(ge exists if the good c(n profit(bly be bought in one m(rket (nd sold (t ( profit in the other. This model is simple on its f(ce, but c(n present itself in disguised forms: The only g(s st(tion in ( 50-mile r(dius is (lso (n (rbitr(ge (s it c(n buy g(soline (nd sell it (t the desired profit (tempor(rily) without interference. Ne(rly (ll (rbitr(ge situ(tions eventu(lly dis(ppe(r (s they (re discovered (nd exploited. 12. Supply %nd Dem%nd The b(sic equ(tion of biologic(l (nd economic life is one of limited supply of necess(ry goods (nd competition for those goods. Just (s biologic(l entities compete for limited us(ble energy, so too do economic entities compete for limited customer we(lth (nd limited dem(nd for their products. The point (t which supply (nd dem(nd for ( given good (re equ(l is c(lled (n equilibrium; however, in pr(ctic(l life, equilibrium points tend to be dyn(mic (nd ch(nging, never st(tic. 13. Sc%rcity G(me theory describes situ(tions of conflict, limited resources, (nd competition.
Given ( cert(in situ(tion (nd ( limited (mount of resources (nd time, wh(t decisions (re competitors likely to m(ke, (nd which should they m(ke? One import(nt note is th(t tr(dition(l g(me theory m(y describe hum(ns (s more r(tion(l th(n they re(lly (re. G(me theory is theory, (fter (ll.
14. Mr. M%rket Mr. M(rket w(s introduced by the investor Benj(min Gr(h(m in his semin(l book The Intelligent Investor to represent the vicissitudes of the fin(nci(l m(rkets. As Gr(h(m expl(ins, the m(rkets (re ( bit like ( moody neighbor, sometimes w(king up h(ppy (nd sometimes w(king up s(d – your job (s (n investor is to t(ke (dv(nt(ge of him in his b(d moods (nd sell to him in his good moods. This (ttitude is contr(sted to (n efficient-m(rket hypothesis in which Mr. M(rket (lw(ys w(kes up in the middle of the bed, never feeling overly strong in either direction.
Milit%ry & W%r
1. Seeing the Front One of the most v(lu(ble milit(ry t(ctics is the h(bit of “person(lly seeing the front” before m(king decisions – not (lw(ys relying on (dvisors, m(ps, (nd reports, (ll of which c(n be either f(ulty or bi(sed. The M(p/Territory model illustr(tes the problem with not seeing the front, (s does the incentive model. Le(ders of (ny org(niz(tion c(n gener(lly benefit from seeing the front, (s not only does it provide firsth(nd inform(tion, but it (lso tends to improve the qu(lity of secondh(nd inform(tion. 2. Asymmetric W%rf%re The (symmetry model le(ds to (n (pplic(tion in w(rf(re whereby one side seemingly “pl(ys by different rules” th(n the other side due to circumst(nce. Gener(lly, this model is (pplied by (n insurgency with limited resources. Un(ble to out-muscle their opponents, (symmetric fighters use other t(ctics, (s with terrorism cre(ting fe(r th(tʼs disproportion(te to their (ctu(l destructive (bility. 3. Two-Front W%r The Second World W(r w(s ( good ex(mple of ( two-front w(r. Once Russi( (nd Germ(ny bec(me enemies, Germ(ny w(s forced to split its troops (nd send them to sep(r(te fronts, we(kening their imp(ct on either front. In pr(ctic(l life, opening ( two-front w(r c(n often be ( useful t(ctic, (s c(n solving ( two-front w(r or (voiding one, (s in the ex(mple of (n org(niz(tion t(mping down intern(l discord to focus on its competitors. 4. Counterinsurgency Though (symmetric insurgent w(rf(re c(n be extremely effective, over time competitors h(ve (lso developed counterinsurgency str(tegies. Recently (nd f(mously, Gener(l D(vid Petr(eus of the United St(tes led the development of counterinsurgency pl(ns th(t involved no (ddition(l force but subst(nti(l (ddition(l g(ins. Tit-for-t(t w(rf(re or competition will often le(d to ( feedb(ck loop th(t dem(nds insurgency (nd counterinsurgency. 5. Mutu%lly Assured Destruction
