Complexity

[Hannan,  Jason  (2011).  “Complexity,”  in  The  Encyclopedia  of  Social  Networking,   George  Barnett,  ed.  London,  UK:  Sage.]     Complexity     Introduction       The  natural  and  social  sciences  have  undergone  a  key  paradigm  shift  in   recent  times:  a  movement  away  from  reductionist  modes  of  analysis,  which  seek  to   understand  natural  and  social  phenomena  by  appeal  to  their  most  basic  and   indivisible  components,  toward  more  holistic  modes  of  analysis,  which  also  seek  to   understand  natural  and  social  phenomena,  but  by  focusing  on  the  relationship   between  the  various  components  within  a  larger  system.    An  emphasis  on  systems   allows  us  to  identify  interesting  patterns  and  mechanisms  that  might  otherwise   escape  our  attention.    One  of  the  features  attributed  to  systems  generally  is   complexity,  arguably  among  the  most  theoretically  exciting  topics  in  the  natural,   social,  and  applied  sciences  today.     Complexity  is  a  notoriously  difficult  concept  to  define,  largely  because  there   is  no  agreement  as  to  what,  exactly,  complexity  entails.    Although  we  are  apt  in   practice  to  distinguish  complex  from  simple  systems,  or  systems  of  greater   complexity  from  those  of  lesser  complexity,  it  is  not  always  apparent  what  precise   standard  or  measure  makes  the  relevant  difference.    We  are  guided  from  the  start  by   implicit  intuitions  about  the  way  systems  work,  and  it  is  by  comparing  different   systems,  or  by  comparing  the  different  historical  or  developmental  stages  within  the   life  of  a  single  system,  that  we  are  able  to  identify  interesting  features  we  associate   with  complexity.     Perhaps  our  most  basic  intuition  about  complexity  is  that  it  involves   quantitative  phenomena:  a  great  many  units  interacting  together  according  to  some   more  or  less  intelligible  order  or  pattern.    However,  what  makes  complexity   interesting  is  that  the  units  within  a  system  act  in  such  a  way  as  to  indicate  at  least   some  degree  of  randomness  and  chance.    Complex  systems  are  not  perfectly  ordered   or  absolutely  predictable;  rather,  they  defy  expectations  through  novel  and   surprising  behavior.    Although  the  element  of  chance  and  uncertainty  indicates   disorder,  it  is  precisely  the  creative  and  dynamic  presence  of  order  and  disorder   that  makes  a  system  complex.    Perfect  order  is  uninteresting,  since  it  is  always   predictable  and  therefore  leaves  no  questions  worth  asking.    On  the  other  hand,   pure  disorder  is  equally  uninteresting,  for  there  is  nothing  to  latch  on  to,  nothing   interesting  to  try  to  understand.    Complexity  captivates  our  attention  precisely   because  of  its  capacity  to  behave  and  react,  a  capacity  partially  predictable  and   partially  unpredictable.     Unpredictability  notwithstanding,  a  complex  system  is  nonetheless   deterministic,  a  feature  associated  with  chaos.    Although  suggestive  of  pure   disorder,  chaos  technically  refers  to  the  capacity  of  a  complex  system  to  exhibit   unstable  aperiodic  behavior  within  a  bounded  range  of  possibility.    What  we  take  to   be  randomness  is  in  fact  governed  by  some  underlying  principle.    There  are  several  

characteristics  of  chaos.    A  system  is  chaotic  if  a)  it  is  dynamic,  or  subject  to  change   and  evolution  over  time;  b)  it  is  sensitive  to  initial  conditions;  c)  great  changes   within  a  system  can  result  from  simple  causes;  and  d)  a  system  is  non-­‐linear,  that  is,   if  its  output  is  different  or  greater  than  its  input.    One  common  metaphor  for  chaos   is  the  “butterfly  effect,”  in  which  a  relatively  miniscule  or  momentary  event  can   result  in  enormous  change  over  an  extended  period  of  time,  a  phenomenon   observable  in  quantum  behavior,  traffic  jams,  and  the  weather.     Another  dimension  of  complexity  is  the  manner  in  which  the  components  of   a  system  interact  with  each  other.      There  might  be  many  different  types  of   components  with  a  single  system,  and  the  manner  in  which  components  of  a  single   type  interact  with  each  other,  and  that  in  which  components  of  different  types   interact  with  each  other,  can  vary  greatly.    The  relationships  between  components   are  often  mediated  in  very  intricate  ways.    The  internal  processes  of  a  system  are   dynamic  when  they  change  or  evolve  over  the  course  of  time.    Emergence  refers  to   complex  behavior  resulting  from  the  interaction  between  a  system’s  components.     That  systems  react  to  external  stimuli  indicates  another  sort  of  interactive   relationship,  that  between  system  and  environment.    One  way  of  understanding  the   complexity  of  a  given  system  is  by  examining  its  capacity  for  adaptation  in  the  face   of  ever-­‐changing  external  stimuli.    Those  systems  that  lack  a  sufficient  capacity  to   adapt  are  liable  to  die  off.    Adaptation  requires  a  feedback  mechanism  permitting  a   system  to  monitor  the  surrounding  environment.    The  cells  in  our  body,  schools  of   fish  in  the  ocean,  and  flocks  of  birds  in  the  wild  are  all  complex  systems  possessing   feedback  mechanisms  by  which  to  adapt  to  changes  in  their  respective   environments,  including  the  presence  of  danger.    The  components  of  a  system  do   not  all  evolve  and  adjust  at  a  single  rate  of  change  in  response  to  environment   stimuli.    It  is  necessary  for  some  portion  of  a  system  to  evolve  and  adjust  at  a  slower   rate  so  as  to  retain  the  continuity  of  the  system.    If  all  the  components  of  a  system   react  too  quickly  to  environmental  changes,  the  system  itself  may  well  dissolve   altogether.     Complexity  and  Social  Networks     Complexity  has  become  a  useful  concept  for  analyzing  social  networks,  both   human  and  non-­‐human.    This  is  because  social  networks  are  systems  that  exhibit   many  of  the  same  features  as  those  found  in  the  examples  discussed  above.    A  good   example  of  this  is  self-­‐organization.    In  the  case  of  ant  colonies,  activist  groups,  or   business  networks,  no  single  entity  is  responsible  for  organizing  the  network  into  an   intelligible  order.    Yet,  these  and  other  social  networks  exhibit  highly  ordered   structures,  in  many  cases  allowing  for  rigorous  quantitative  analyses.    Generally,  it  is   found  that  the  individual  entities  or  groups  within  a  social  network  informally   follow  basic  rules  that  allow  for  a  remarkable  degree  of  consistency  and   predictability.    Recent  studies  have  also  applied  complexity  theory  to  understand   structuration,  the  process  by  which  agents  and  social  systems  dialectically  shape   and  influence  each  other.    Other  studies  have  focused  on  the  role  of  complexity  in   small  groups,  the  internal  dynamics  of  which  differ  markedly  from  those  of  larger   social  entities.    Other  studies  still  employ  complexity  to  understand  decision  

development,  which  concerns  how  interaction  within  an  organization  leads  to  the   emergence  of  collective  decisions.   Homophily  refers  to  the  tendency  of  like  entities  to  form  a  network  together.     In  the  case  of  human  networks,  homophilous  relationships  are  determined  by   certain  characteristics,  such  as  ethnicity,  gender,  language,  sexual  orientation,  class,   culture,  politics,  aesthetic  pursuits,  occupation,  institutional  affiliation,  and  so  forth.     Given  the  different  sorts  of  identification  possible  for  a  single  individual,   membership  in  multiple  networks  is  quite  common.    Thus,  a  single  individual  may   be  a  member  of  a  network  consisting  of  speakers  of  a  common  language,  while  being   a  member  of  another  network  consisting  of  members  of  a  common  profession.     Sometimes,  these  networks  overlap,  but  not  always.     One  lively  area  in  the  study  of  complexity  is  the  evolution  of  social  networks.     Social  networks  exhibit  evolutionary  patterns  in  many  ways  similar  to  those  of   biological  species.    Social  network  analysis  examines,  among  other  things,  how   environmental  stimuli  compel  a  network  to  change  and  evolve.    In  the  case  of   scholarly  networks,  terrorist  networks,  and  Star  Trek  fans,  evolution  is  prompted  in   part  by  information  from  the  surrounding  environment.    How  such  information  is   processed  within  a  network  determines  in  part  how  it  will  evolve.     Different  Approaches  to  Complexity  Theory     It  is  important  to  note  that  the  question  of  complexity  is  not  approached  in  a   uniform  way.    In  physics,  engineering,  and  computational  analysis,  the  study  of   complexity  is  largely  quantitative  and  is  characterized  by  statistical  analysis  and   agent-­‐based  modeling.    There  is,  however,  a  more  philosophical  approach  to   complexity,  one  which  draws  very  heavily  from  the  concepts  of  biology  (in  which   the  idea  complexity  also  has  widespread  application),  but  which  is  used  to  theorize   about  the  nature  of  knowledge  and  society.    The  latter  is  characteristic  of  cybernetics   and  systems  theory,  a  theoretical  tradition  within  sociology  represented  by  such   figures  as  Talcott  Parsons,  Gregory  Bateson,  and  Niklas  Luhmann.         Jason  Hannan   Northwestern  University     See  Also:  Cooperation/Coordination,  Game  Theory  and  Networks,  Homophily,  Self-­‐ organizing  Networks     Further  Reading     Brown,  S.  L.,  &  Eisenhardt,  K.  M.  “The  Art  of  Continuous  Change:  Linking  Complexity   Theory  and  Time-­‐Paced  Evolution  in  Relentlessly  Shifting  Organizations,”   Administrative  Science  Quarterly,  No.  42,  1997,  pp.  1-­‐34.   Cilliers,  P.  Complexity  and  Postmodernism:  Understanding  Complex  Systems.  London,   UK  &  New  York,  NY:  Routledge,  1998.   Katz,  N.,  Lazer,  D.,  Arrow,  H.,  Contractor,  N.  “Network  Theory  and  Small  Groups,”  in   Small  Group  Research,  Vol.  35,  No.  3,  June  2004,  pp.  307–332.  

Luhmann,  N.  Social  Systems.  Translated  by  John  Bednarz,  Jr.  and  Dirk  Baecker.   Stanford,  CA:  Stanford  University  Press.   Mitchell,  M.  Complexity:  A  Guided  Tour.  Oxford,  UK:  Oxford  University  Press,  2009.   Morin,  E.  On  Complexity.  Cresskill,  NJ:  Hampton  Press,  2008.   Taylor,  M.  C.  The  Moment  of  Complexity:  Emerging  Network  Culture.  Chicago,  IL:   University  of  Chicago  Press,  2001.