Barteld Braaksma (based on work by Gert Buiten, Edwin de Jonge and Frank Pijpers)

1. Complexity and networks Overview of CBS work and approach Barteld Braaksma (based on work by Gert Buiten, Edwin de Jonge and Frank Pijpers) 15 February 2019, DIME/ITDG SG

2. Context: ‘A tale of two papers’ Applications of Complexity Theory at CBS Steering in an interwoven dynamic (Ministry of Economic Affairs) 2

3. Structure of presentation • Introductiontocomplexity • Overview of applications at CBS • Networks • Agent BasedModels (ABMs) • Discussion 3

4. Complexity (very) short • A complex system consists of partswhoseindividualbehaviour leads tomutualinteractionsthat, at the level of the system, show ‘complex behaviour’ • “The whole is more thanthesum of itsparts”. • ComplexityScienceand Network Science are new scientificdomainsthat have developed tools to get a grip on this kind of systems 4

5. Example 1: game of life (Conway) 5

6. Example 2: Hénon map ‘Strange attractor’ 6

7. What approach forstatistics? Use the paradigms of the physical sciences: • Start with a simple model of a system we want tounderstand • Usethe model topredict a property or behavior of that system whichcanbemeasured in the real-life system (statistical tests) • If/whenthetwodiffer: extendthe model • Repeat • Experiment!

8. How canthis reductionist approach lead tocomplexity ? • The word simplereferstothe set of rulesdescribingtheinteractionsbetweenthemanyentitieswhichtogether form the system • Everyentity (agent) in the system has a finitelikelihood of interactingwithanyotherentity • The emergentbehaviour (observables!) of the systembecomes complex (non-linear, withdelays/hysteresis, phasetransitions,….) • ABM: defineinteractions in terms of a (Markov) process: thelikelihood of theoutcome of everyinteractiondepends on thevalue of theproperties of theentities at input (discrete time-stepping).

9. Applications of Complexity at CBS Create network databases Network analysis Development of sample survey theory for networks Agent Based Models 9

10. Network approaches • People / companies or even a combination • (CBS has partial information of network) • Sensors/IoT (e.g. road sensor data) or aggregates (business sectors) • (un)directed (weighted) graphs : quantifythetopology, look fore.g. communitiesor valuechains Implicit : the links in the networks have a high persistence. Any dynamics / behaviour arises from a ‘signal’ travelling along the vertices between nodes.

11. Applications of network analysis Structures and patterns of the network as a whole Description of clusters, chains and communities Regional analyses Analysis of robustness, vulnerabilities and system risks Basis for Agent Based Models PM: Improvement of existing statistics (e.g. I/O tables) 11

12. Social Network ‘The Netherlands’ • People depend on their network • Employment. Finding a job • Care. Increasingly provided by relatives, friends, neighbours,.. • Values and opinions. Often spread via peer network • Social in/exclusion. Access to the right people; ‘Yellow vests’ • Technical advantage: some statistics may be naturally derived from graph or network dataset 12

13. Network NL:16.9 mln nodes3.3x10^14 links possible 39 bln links realised1800 (avg)links p/p 13

14. How many parents have children within 10km? 14

15. Network database of persons (Rotterdam) The brighter the colour, the more likely that people stay in their own community (measure of segregation) 15

16. Rotterdam is a small world Size of subgraphs 16

17. Economic network dataset In fact a ‘weighted, directed network’. The network can be estimated at microlevel using digital sources like financial transactions and customer databases c/w new estimation methods Database of trade relations between Dutch companies being derived. Several extensions envisaged Next to traditional methods like I/O-analysis specific methods for network analysis and Agent Based Models may be used. Think of detection of bubbles, turning points, winner-takes-all markets, systemic risks, clusters and value chains, labour market effects, ... 17

18. Example 1: London is the center of the corporate world 18

19. Example 2: Cluster analysis 19

20. Agent Based Models • For CBS interesting to interpreting results (a.o. statistical distributions), develop statistics and indicators, and understand new phenomena • For users interesting for e.g. policy development and scenario analysis • Toolkits available to derive ABMs • Often requires massive amounts of data 20

21. Agent Based Models Testing of hypotheses Exploration Simulation Statistics Dashboards Visualisations Maps Reality Insight and interpretation (Early warning) Indicators Network structures Imputation methods Statistical model Agent Based Model Units Attributes Variables Empirical data Actors Interactions Feedback mechanisms

22. ABM for digital security • A flexible model was developed and applied to the spreading of computer viruses • Actors possess one or more ‘devices’ • The devices create a network • Actions are: • Creating or breaking relations • Using relations to change ‘status attributes’ of devices, relations or actors(e.g. contaminated, virus scanner in place, software update, download of files, transfer of money, product delivery, ... 22

23. Example 1: spreading and type of network 23

24. Example 2: effect of consumer behaviour Bij deze plaatjes trapt vijftig procent van de mensen bij een ‘virusmail’ erin waarna hun apparaat besmet raakt. Door die vijftig procent ontstaat er een vertraging in de verspreiding van het virus. In de volgende ronde verwijdert de actor na besmetting het virus en wordt vervolgens ‘immuun’. Je ziet nu dat de maximale besmettingsgraad lager is dan in de eerdere plaatjes. Verder zie je opnieuw dat het type netwerk van invloed is op de snelheid waarmee een virus zich verspreid – en daarmee ook weer op de maximale besmettingsgraad. 24

25. Example 3: effect of consumer behaviour Bij dit plaatje geldt hetzelfde als bij voorbeeld 2 maar trapt maar tien procent erin bij een virusmail, waardoor de vertraging groter wordt. 25

26. ABM for digital platforms • Simple example of a market of fast-food (snack) restaurants when an online ordering-delivery service arrives • Each restaurant has its own catering area • Households have a propensity to change when their own or a neighbouring restaurant offers online delivery • Restaurants are likely to offer online service when they expect a profit 26

27. Example 1: slow introduction, then a big bang • Not all restaurants move over • Part of the profit goes to online platform • One in five restaurants not profitable

28. Example 2: quick introduction, gradual increase of orders • Almost all restaurants move over • Relatively little restaurants make a loss

29. Example 3: quick introduction, only part of restaurants moves over • Restaurants that do not move over make a loss

30. Discussion • Focus on relations betweenobjectsandbehaviourinstead of objectsthemselves • Potentialtocontributeto real-life scientificand policy questions • Specific area (nottouchedyet): IoT-> trusted smart statistics • Construction of networksrequires a lot of linkeddata (big data, admin registers, IoT) • Huge demands on computational power: HPC, smart algorithms • Analysis of networks: how far should we go? • System approach; model-basedstatistics. Whatassumptions are allowed? • Manyopportunitiesforworkingwithscientificcommunity andother partners • Very relevant new research area! 30

31. Factsthat matter