Dieckmann, U.

International Institute for Applied Systems Analysis (IIASA)

UnexpeC lomp ex I i ht f Ans g s rom  Ulf Dieckmann (Ev IntroductionThe principle of fitness maximization is widely employed in contempora         traits vary with empirically observed environmental conditions      .Yet two fundamental determinants of real social systems and ecosyste,         frequency-dependent selection and realistic density regulation From a     .  observed adaptations do not usually maximize any absolute measure o         fdependent evolutionary dynamics determined by the interplay o  trait-dBuilding on these insights the theory of adaptive dynamics actively de   ,     ,  revealing previously unexpected and often counterintuitive system be  ,   ,  evolutionary self-extinction, and evolutionary cycling.    EvolutionaryE l ti b hivo u onary ranc ng ecreation of di ersit It oBranching pointrait  v y.  e trcounterintuitively convergetivedaptthat directional selectionAd    selection. Evolutionary b  ti ll lti iDi ti l l ti Di ti l ti sequen a y, resu ng n corec ona  se ec on srup ve se ec onTimeE ol tionar Sv u yP l  ower- awtait)Owing to evolutionary slowinexponen1/3ve tra   −aptivtare not approached exponeAdatrait   g(ΔAve tslow. The resultant powelogptiv    “ i d f t it ” fdapw n ow o  oppor un y  or alog(Time)Adand the power law expone  -  dimensionality n of the   TimeEvolutionary S DisEvolutionary self-extinctiontysco   nsi ontiof an adaptive trait indudeinu     d l i l ti dum ousun er y ng popu a on ybriu s bdiscontinuous transition touilib ifu  Equ Selection pressurercasystem’s gradual adaptaE atio   ndemAd i iapt ve tra tEvolutionaE l ti li ltvo u onary cyc ng resurace between traits or popul    ssthe underlying fitness landscnes   Fitnreshaping underfoot, shoF  d i lt i iynam cs can resu  n ncnever settle on any on    Ad ti t itap ve ra st d B h i ic e e av ors n  Ad ti S tap ve ys ems: d ti D i Thap ve ynam cs eory  olution and Ecology Program IIASA)   , ry studies of adaptation to predict how empirically observed adaptive          ms have remained unaccounted for in this classical approach:         n evolutionary perspective these are just flipsides of the same coin:  ,         f fitness but must instead be understood as outcomes of frequency- ,         ependent and density-dependent processes.veloped at IIASA is opening up new vistas on studies of adaption  ,         , haviors such as evolutionary branching evolutionary slowing down,    ,   , Branching l i th dxp a ns e en ogenous cc rs hen e ol tion raitu  w  v u  etrs to a fitness minimum so tive    ,  daptis followed by disruptive Ad    ranching can also occur A t i i t ti lt i li tt f    l l ti tsymme r c n erac ons resu  n cyc c pa erns ol ti b hi d l ti d i ti timp ex evo u onary rees. evo u onary ranc ng an  se ec on- r ven ex nc onTimelo ing Do nw w g down adaptive outcomes ,   ntially fast but algebraically ss    tner law reflects the closing Fit     d t d t tivan ageous a ap a ons, nt (2n+1)−1 reveals the −   underlying trait space  .Adaptive traitelf Extinction E- EquInfinite-alleleltil tiDiallelicltil tiuilibmu ocus gene cs mu ocus gene csoccurs when the evolution es) briu    ade um ces a bifurcation in the (sha po     i th t i l ait ( pulnam cs a  nvo ves a tralatiextinction In this way a ive on  .   ,  apti siztion can cause its own Ada ze (     A (lineisees).TimeCry ycling Individual-basedRandom-walkGradient-ascent modelmodelmodelf d tis rom an a ap ve arms ations As evolution climbs its.    traiape this landscape keeps ve t,    ptivwing how endogenous Adap   t d t ti th tAessan  a ap a ons a  e stationary outcome  .Time

Unexpected behaviors in complex adaptive systems

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