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ENoLL FAO Workshop Francisco Molinari 2
 

ENoLL FAO Workshop Francisco Molinari 2

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PaESI Concept Note and Technical Architecture An Experiential Platform for the Participatory Evaluation of Sustainable Innovation in Livelihood Conditions ...

PaESI Concept Note and Technical Architecture An Experiential Platform for the Participatory Evaluation of Sustainable Innovation in Livelihood Conditions

FAO ENoLL Workshop 25-26 January 2011

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    ENoLL FAO Workshop Francisco Molinari 2 ENoLL FAO Workshop Francisco Molinari 2 Presentation Transcript

    •   Joint  FAO-­‐ENOLL  meeting  Rome,  26-­‐01-­‐2011   PaESI Concept Note and Technical Architecture An Experiential Platform for the Participatory Evaluation of Sustainable Innovation in Livelihood Conditions Francesco Molinari
    • 2   TOC    Ra-onale     Sustainable  Development  as  Systemic  Change     An  alterna-ve  point  of  view    Concept     Evidence  based  decision  making     Immersive  /  experien-al  plaHorm     Digital  ecosystem    Func-onal  descrip-on    Conclusions  
    • 3   Rationale    Sustainable  development  is  a  maJer  of  systemic  change  that  (posi1vely)   impacts  on  the  economic  drivers  of  a  community,  while  preserving  the   fundamental  characters  of  the  social  and  environmental  landscape.      However,  evalua-ng  the  progress  “towards  a  beJer  future”  of  a  given   community  is  mostly  a  foresigh1ng  exercise,  possibly  grounded  on  con-ngent   pieces  of  evidence,  which  can  only  en-tle  to  work  out  “reasonable”   specula-ons  –  quite  oQen  becoming  “seIf-­‐fulfilling”  expecta-ons.    An  alterna-ve  point  of  view  takes  as  a  baseline  for  defining  sustainable   innova-on  (of  a  policy  stance,  or  a  concrete  instance  thereof)  the  actual   livelihood  condi1ons  of  the  people  affected  by  its  envisaged  implica-ons,  in   terms  of  e.g.  available  housing,  nutri-on,  health  records,  levels  of  educa-on   etc.    
    • 4  
    • 5   Rationale  (2)    In  principle,  the  various  elements  forming  this  baseline  can  be  analysed,   interpreted  and  even  visualised,  to  help  governments  make  more  informed   decisions  on  systemic  change,  and  especially  to  “fine  tune”  their  ongoing   policy  ac-ons:     either  objec1vely,  i.e.  by  measuring  and  benchmarking  some  quan1ta1ve   indicators  of  performance,  in  terms  of  an  “assessed  impact”  projec-on  over  -me;       or  subjec1vely,  i.e.  by  integra1ng  people’s  “wishes  and  wills”  into  the  decision-­‐ making  process,  to  arrive  at  a  “perceived  impact”  evalua-on  with  the  help  of  the   ci-zens  directly  or  poten-ally  affected  by  the  ac-on’s  results.    What  is  missing  in  this  scenario  is  the  introduc-on  of  a  permanent,  “evidence   based”,  decision  support  plaGorm,  assisted  by  (though  not  limi-ng  itself  to)   the  use  of  ICT  tools,  and  open  to  the  par-cipa-on  of  a  huge  number  of   people,  not  only  stakeholder  representa-ves,  but  mostly  coming  from  the   general  public  of  a  given  jurisdic-on.  
    • 6   Concept    The  plaHorm  should  offer  to  people  the  possibility  of  ge]ng  immersive   experiences  –  from  which,  the  “experien1al”  aJribute  used  to  name  it  –   during  the  configura-on  and  valida-on  of  future  scenarios  concerning  a   community’s  growth  and  development.      The  ongoing  evalua-on  of  public  policies  done  through  the  plaHorm  should   reflect  a  pragma1c  combina1on  of  objec1ve  and  subjec1ve  indicators,  thus   responding  seamlessly  and  dynamically  to  the  evolving  needs  and   requirements  of  the  par-cipants,  rather  than  depending  on  the  usual  -me   lags  in  the  collec-on  and  elabora-on  of  performance  related  evidence.    Finally,  people’s  engagement  in  the  plaHorm  ac-vi-es  should  be  con1nuous   and  uninterrupted,  thus  going  far  beyond  the  occasional,  “one-­‐shot”   experiments  where  ci1zens  are  used  as  “guinea  pigs”  in  a  guided   par-cipatory  environment  and  pathway.  
    • 7   Concept  (2)     The  PaESI  solu-on  comes  from  original  in-­‐house  research  efforts  done  at  our   Company  in  the  framework  of:   1.  an  assessment  study  of  the  Living  Labs  “phenomenon”,  promoted  by  the   European  Commission  in  2008,  where  an  updated  version  of  the  MASAI  Approach*   was  developed  and  implemented,  focusing  on  the  transi-on  phase  of  European   Living  Labs  from  R&D  towards  industrially  and  commercially  viable  solu-ons;   2.  an  innova1ve  start-­‐up  project  (www.supermoney.eu),  funded  by  the  Italian   Ministry  of  Youth  Innova1on,  which  has  deployed  a  web  service  to  households   and  businesses,  allowing  the  comparison  of  prices  and  other  commercial   condi-ons  of  several  mass  consump-on  goods  and  services  –  from  fixed  and   mobile  telephony  to  the  most  popular  banking  and  insurance  contracts.     It  also  takes  into  account  the  ongoing  experience  at  FAO  in  the  collec-on  and   delivery  of  livelihood  datasets  (e.g.  hJp://kids.fao.org/glipha/index.html)    * The MASAI basic approach has first been developed by MTA in 2004 and used for a Study of the Impact of the IST Programme, and of itspredecessor Programmes Esprit IV, ACTS, TAP (Contract N° C28262 with DG INFSO).
    • 8   Concept  (3)    The  PaESI  plaHorm  is  also  based  on  the  no-on  of  “digital  ecosystem”,  a  Living   Lab-­‐like  community  resul-ng  from  a  con-nuous  interac-on  among  the  three   cons-tu-ng  elements  of  any  “real-­‐life”  socio-­‐economic  space:     -­‐  the  people,  in  their  various  roles  (e.g.  ci-zens,  customers  etc.),     -­‐  the  (private  and  public)  ins1tu1ons  popula-ng,  delimi-ng  and  regula-ng  the   people’s  living  spaces,  and     -­‐  the  (natural  and  built)  environment  they  all  live  in.    Like  any  biological  system,  a  digital  ecosystem  aims  first  to  its  own  growth  and   development,  thanks  to  the  con-nuous  inclusion  and  selec-on  of  new  users   and  interac-on  among  its  cons-tu-ng  elements.  The  system  can  also  let  some   new  and  addi-onal  self-­‐organising  structures  emerge,  which  enhance  its   compe--veness  with  respect  to  other,  less  powerful  or  efficient,  structures.   However,  its  final  target  is  always  to  reach  a  cri-cal  mass,  allowing  the  so-­‐ called  “autopoiesis”,  or  “network  effects”,  typical  of  complex  systems,  a   phenomenon  that  can  be  managed  and  governed  by  the  use  of  appropriate   methods  and  tools.  
    • 9   Functional  description  Decision Scenario Scenario Scenario • Ranking of priorities Makers A B N • Informed decisions 1 •  Provision of indicators •  Definition of scenarios •  Feedback PaESI 3 collection • Immersive scenariosPlatform for evidence • Shared future visions based decision • Transparent alternatives making •  Self-Profiling 2 •  Analysis of indicators •  Selection of scenarios • Active engagement Users User User User • Improvement of own #1 #2 #X livelihood conditions
    • 10   Functional  description  (2)     In  its  current  implementa-on,  our  proposed  system  architecture   leverages  on  four  different  components  and  data  sources:   1.  an  informa1on  base,  which  is  created  and  configured  since  the   1   project’s  ini1alisa1on,  and  is  then  con-nuously  monitored  and  updated   at  the  level  of  M2M  interac-on,  i.e.  via  a  permanent  dialogue  with  the   customer  organisa-ons’  [ins-tu-ons’]  IT  systems   2.  a  computa1onal  model,  framing  the  collec1on,  integra1on  and   localisa1on  of  user  data  (profiles,  indicators,  explicit  and  implicit   preferences,  needs,  requirements  etc.)  and  possibly  environmental  data   2 (deriving  from  available  pieces  of  evidence)   3.  user  generated  datasets,  collec-ng  the  inputs  provided  by  the   par-cipants  while  being  put  in  rela-on  with  the  contents  of  the  3   informa-on  base   4   4.  system  generated  datasets  (outputs),  offering  on-­‐the-­‐fly  support  to   informed  decision-­‐making.  
    • 11   Conclusions    The  plaHorm  is  being  developed  thanks  to  the  support  of  EU   funding  in  the  context  of  the  CIP  (ICT-­‐PSP)  project  HABITATS.   Its  broad  aim  is  to  leverage  the  intelligence  of  all  relevant   actors,  knowledge  and  datasets,  in  co-­‐designing  sustainable   livelihood  projects  and  programmes.      We  believe  this  concept  and  plaHorm  can  be  successfully   adapted  to  the  purpose  of  performing  a  par-cipatory   evalua-on  of  the  sustainability  of  innova-on,  as  well  as   providing  a  valid  and  updated  informa-onal  support  to  public   decision  makers  in  a  number  of  related  domains.