Tricks of the trade: generating ideas for interdisciplinary research

1. Tricks of the trade:generating ideas for interdisciplinary research Alan Wilson The Alan Turing Institute March 2017

2. Purposes • how to think about doing something ‘new’ • ‘thinking outside the box’ • being ambitious • beyond the conservatism of referees and the REF • based on • Knowledge power (2010) • quaestio.blogweb.casa.ucl.ac.uk • Tricks of the trade – a compendium of blog pieces

3. PART 1. HOW TO DO RESEARCH.

4. Basic principles • STM (for analytics) • S – the systems of interest • T – the theory • M – methods • PDA (for real challenges) • P – policy • D – design – invention of plans • A - analysis

5. Interdisciplinarity • research needs all relevant knowledge as its base – implies interdisciplinarity • a systems’ focus drives this • ‘spatial analysis’ is interdisciplinary • as is ‘urban science’ or ‘regional science’ • NB, however, the power of disciplines, especially economics!

6. A systems focus on cities • living in cities – people issues • the economy of cities • urban metabolism: energy and materials flows • urban form • infrastructure • governance

7. living in cities – people issues • housing, there is a current shortage and this situation will be exacerbated by population growth • education – a critical service – upskilling for future proofing and yet a significant percentage leave school inadequate in literacy, numeracy and work skills • health – a postcode lottery in the delivery of services? • the future of work – what will happen if the much predicted ‘hollowing out’ occurs as middle-range jobs are automated? How will the redundant pay their bills?

8. the economy of cities • the ‘economy’ embraces private and public and so has to deliver products, services and jobs (and therefore incomes) • urban metabolism: energy and materials flows • issues of sustainability and the feasibility – indeed the necessity – of achieving low carbon targets • urban form • where will the necessary new housing go – 200,000+ p.a. for the foreseeable future?

9. infrastructure • accessibilities are crucial for both people and organisations so transport infrastructure and an effective system are correspondingly critical • investment in utilities will be necessary to match population growth but also to respond to the sustainability agenda • in particular, counting communications and broadband as utilities, how do we secure our future in a competitive world? • governance • at what levels are planning and policy decisions best made? • the security of food, communications and utilities? This brief analysis throws out one immediate important conclusion: these issues are highly interdependent and one important area of research is to chart these interdependencies and to build policies and plans that take them into account.

10. Real challenges • much of our research is on cities and regions • we are lucky in that much of it is often immediately applicable – meeting real challenges • which brings to mind: any research must be interesting and important – the former to you, the latter to someone else – and if it relates to real challenges, that can help

11. ‘Research on’ vs ‘research for’ • follows from the previous slide • most academic research is research on ‘systems of interest’ – notably aspects of cities or regions; this is true of many Business Schools too – on firms rather than for firms • so always think about the organisations in your research – is there a ‘research on’ aspect of it?

12. Serendipity • the best research idea I ever had – using the concept of entropy in building spatial interaction models – came about because I had once, in an earlier life, specialised in statistical mechanics • how many good research ideas come from being able to trawl across a wide range of reading and studying and recombining? • cf. ‘combinatorial evolution’ to come ……

13. Following fashion • why is so much current research on • agent-based modelling • network analysis • social media • big data • smart cities? • because it is the fashion. Is it best to follow fashion as a researcher or does this imply you are too late?! • cf. ‘Against oblivion’ to come ……

14. Questions on ……… • STM • PDA

15. PART 2. SUPER CONCEPTS

16. Systems thinking • the ‘system of interest’ and ‘entitation’ • then • granularity (scale)? • how to treat space – continuous or discrete? • how to treat time? • recall STM and PDA

17. Complex systems; complexity science Warren Weaver (1950s) identified three kinds of system: • simple systems • systems of disorganised complexity • systems of organised complexity and then argued that the most challenging research problems would be concerned with the third

18. Combinatorial evolution • Brian Arthur, The nature of technology • key ideas: • technologies are made up of hierarchies of systems • discoveries are usually made at lower levels • or though new combinations at lower levels • can be applied to ‘research’ and ‘science’ • so structure your problem hierarchically and look at the lower levels

19. We can represent this and three (speculative) lower levels broadly as follows. • level 1: working model – static or dynamic • level 2 – cf. STM, (cf. How to begin): • system definition (entities, scales: sectoral, spatial, temporal); exogenous, endogenous variables • hypotheses, theories • means of operationalising (statistics, mathematics, computers, software,…) • information system (cleaned-up data; intermediate model to estimate missing data) • visualisation methods

20. level 3: • explore possible hypotheses and theories for each subsystem • data processing; information system building • preliminary statistical analysis • available mathematics for operationalising • software/computing power • level4: • raw data sources An Arthur-like conjecture might be that the innovations are likely to emanate from levels 3 and 4. In level 3, we have the opportunity to explore alternative hypotheses and to refine theories. Something like utility functions, profits and net benefits are likely to be present in some form or other to represent preferences with any maximisation hypotheses subject to a variety of constraints (which are themselves integral parts of theory-building). We might also conjecture that an underlying feature that is always present is that behaviour will be probabilistic and so this should always be present. (In fact this is likely to provide the means for integrating different approaches.)

21. Requisite knowledge • another icon: Ross Ashby, Design for a brain etc • the idea of ‘requisite variety’ in a control system: the controller must have at least as much ‘variety’ as the system being controlled • convert this idea to the knowledge base of your research: what is your ‘requisite knowledge’?

22. The brain as a model • Stafford Beer’s analysis in Brain of the firm • the brain has five levels • final action • specification of action • the autonomous nervous system • ???? • the strategic level • level 4 is a filter to handle information overload • interesting for organisations – usually missing – and for how we approach research – how do we filter? • and a challenge for researchers!

23. DNA • start to think about change over time – the dynamics of a system • it is intuitively clear that the future depends on the past – e.g. future urban structure will in part depend on the existing urban structure • technically, this means ‘depends on the initial conditions’ – the ‘DNA’ – and connects to the idea of path dependence in complexity science

24. Territories and flows • much spatial analysis is concerned with territories – location - and flows - spatial interaction • there are lots of basic concepts here that should be in everyone’s toolkit • the family of spatial interaction models • how different theories of interaction connect • when spatial interaction models become location models • catchment populations • and lots more ……..

25. Super concepts – more examples • 1: system • 2: system representation • 3: location • 4: interaction • 5: accounts • 6: scales • 7: hierarchy

26. 8: complexity • 8a disorganised • 8b organised • 9: entropy • 10: information • 11: variety, requisite variety • 12: control • 13: (system) model • 14: theory

27. 15: understanding • 16: explanation • 17: (flight) simulators • 20: accounts • 21: conservation principles • 22: optimisation • 23: constraints, as representing system knowledge • 24: pattern recognition

28. 25: combinatorics • 26: multiple equilibria • 27: path dependence • 28: interaction, flows • 29: location, nodes • 30: function • 31: structure

29. 32: performance indicators: • a. organisational efficiency • b. catchment populations • c. effective delivery • 33: networks • 34: shortest path in a network • 35: fast and slow dynamics • 36: equilibrium

30. 37: nonlinear systems • 38 critical points • 39 initial conditions • 40: emergence • 41: prey-predator model • 42: competition-for-resources model • 43: periodic solutions • 44. chaos theory

31. 45: Mintzberg’s organisation types • a. simple • b. family • c. divisional • d. conglomerate • e. professional bureaucracy • f. adhocracy • 46: central nervous system (VSM) structures • 47: the ‘war room’ • 48: the business excellence model

32. 49: complexity theory • 50: general systems theory • 51: cybernetics • 52: control systems • 53: computer models • 54: computer visualisation

33. 55: microsimulation • 56: computer algorithms • 57: graphical models • 58: intelligent search • 59: neural networks • 60: adaptive systems

34. Questions on super concepts • additions?

35. PART 3. TRICKS OF THE TRADE

36. Adding depth • this idea is best illustrated through modelling • to build a new computer model – say of a city transport system, say with new ‘methods’ – it is often better to begin by building a ‘toy’ model – which implies coarse scales, sectorally, spatially and temporally. • and then progressively add depth …..

37. Against oblivion • this is the opposite of ‘following fashion’! • it is sometimes worthwhile to mine the classics, in my case • searching for classical rather than the more fashionable quantum statistical mechanics • and building on the work on Lotka and Volterra (and in the process discovering some fascinating characters who did much more, and interesting work, than is now commonly recognised

38. Sledgehammers for wicked problems • it is not difficult to produce a list of ‘wicked’ problems: • social disparities • the refugee crisis • housing in the UK • traffic congestion • etc • they are wicked because they are impossible and defy analysis • so why not try to apply brute force and escape from ‘the box’?

39. Beware of optimisation • it is often attractive to formulate a model as an optimisation problem • economics is riddled with this – ‘rational man’ maximising utility etc • reality is almost always more complicated • for me, this has been part of the power of entropy-maximising – it’s a kind of optimum blurring to get closer to reality

40. Spinning out: ‘research for’ • if any of your research is of the ‘research for’ variety, it is worth thinking through how you communicate it to the ‘for’ • consultancy is a possibility and that can, sometimes, grow into more organised activity as a spin-out company • the blog describes the successful but relatively short history of GMAP Ltd

41. GMAP Ltd • December 1984 • Wetherby Racecourse, Boxing Day • 1985: tour of management consultants • DIY judged to be the only way forward • ULIS as a vehicle, but no real support

42. 1985-7: earliest days (all ULIS contracts) • marketing • mainly through Sunday Times job adverts • Post Office, dry ski slopes (Birmingham) • NB: still mainframe computing at this stage

43. the breakthroughs - 1986/7 – both via Sunday Times: • WH Smith (1986); Toyota (1987) • workforce: AGW, MC; a small amount of hourly-paid help • turnover: £20-100k in first three-four years; £290k p.a... in 1988, when GMAP became a formal division of ULIS • 85/6?: first full-time employee

44. growth - new clients - examples: • DIA (information system breakthrough) • Mansfield Breweries • Whitbreads • Storehouse • Nat West • Barclays • Leeds Permanent (1988) • big breakthrough: FORD - RADAR - late 1989

45. turnovers, 1991-1997 91: £1.4M (25 staff) 92: £1.5M 93: £2.6M 94: £3.1M 95: £4.8M (10 years on) 96: £5.1M 97: £5.8M (110 staff)

46. The sales • 1997: automotive part of GMAP to Polk: • Ford, Toyota, 70 staff • rest of GMAP to minority shareholders • 2001: rest to Skipton Building Society

47. EXAMPLES • Opening stores e.g... W H Smith • network optimization Ford • outlet performance benchmarking Storehouse • retail formats BP/Mobil • sales territory planning SKB • merger and acquisition Halifax

48. NB: the intellectual capital of GMAP consists of • the ideas - not patented • the staff • the capability to accomplish what others haven’t been able to do with those ideas • the ability to work with (potential) client on their problems • sometimes to be able to demonstrate to them what their problems are

49. rate of progress: t/over always behind what you think it should be • though eventually start setting more realistic targets • development issues: • need for a partner • mainly for better access to clients • or sale etc.....

50. issues for the academic entrepreneur • must be committed to the enterprise yourself • find the best route - which may evolve: • consultancy • patenting, licensing • company formation • financing, management • and then the best support • ULIS, RSU, external advisers, venture capitalists, …