Decision support for crowd management

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1 Decision support for crowd management HUMANE Workshop, Oxford 21 March 2017 Dr J Brian Pickering, IT Innovation Centre, University of Southampton jbp@it-innovation.soton.ac.uk 1

Circumstances can force almost anyone to be a bystander to evil, but they can also bring out our own inner hero (Franco & Zimbardo, 2006-2007) Research has shown

(Donald & Canter, 1992) [ ] there was no evidence of mass panic on July 7th, almost no personally selfish behaviour amongst survivors (despite survivor guilt),

2 Circumstances can force almost anyone to be a bystander to evil, but they can also bring out our own inner hero (Franco & Zimbardo, ) Research has shown that the behaviour of people in fires and other emergencies is characterized by internally rational, socially and cognitively structured action. (Donald & Canter, 1992) [ ] there was no evidence of mass panic on July 7th, almost no personally selfish behaviour amongst survivors (despite survivor guilt), and plenty of evidence of mutual helping and even self-sacrifice. (Drury & Cocking, 2007) Michael Foran [CC BY 2.0 ( via Wikimedia Commons 2

3 evacuate: a social scientist s picnic 3

4 MOTIVATION AGENCY OUTGROUP TRUST SPECIAL SENSORS SERVICES EVACUEES CAMERAS SENSORS OPERATIONS OPERATIONAL STAFF AD HOC & SECURE NETWORK USER DEVICES DECISION SUPPORT SYSTEM EMERGENCY SERVICES SEEDS 4

5 Complex Human-Machine Networks are always subject to change planned, expected, serendipitous There needs to be a way to represent and understand that change Normal Operational Mode During a Crisis But there s also a need to consider underlying factors and drivers for that network: Motivation Agency Trust

6 or legal? Decision support: an ethical dilemma 6

7 What information could we and should we be collecting? SENSORS OPERATIONAL STAFF Who is really responsible and for what? CAMERAS OPERATIONS SENSORS DECISION What devices can and should we access? USER DEVICES SUPPORT SYSTEM What decisions are possible? And what should be allowed? MOTIVATION AGENCY TRUST 7

8 Hildebrandt (2015, 2016) and others continue to caution about the power of technology to be able to process data and make decisions about users. PERCEIVED RISK REGULATION Regulation tends to focus on empowering users to make appropriate and informed decisions. Though they may not be willing ( org) or able to exercise that right appropriately (Miltgen, 2015). MOTIVATION AGENCY TRUST 8

9 Agency: who s in control? 9

10 This is what I want to do This is what I need to do This is what needs to be done AGENCY What does the technology allow? What can it do better? What can it help me do? SELF-EFFICACY What is possible with this HMN? Human and Machine Agency may be complementary? How does the one affect the other? MOTIVATION REGULATION PERCEIVED RISK TRUST 10

11 Trust: why might HMNs succeed? 11

12 REGULATION PERCEIVED RISK TRUST Behaviour in HMN MOTIVATION Social Norms HUMAN AGENCY SELF-EFFICACY MACHINE AGENCY Implications Recommendations Design Solutions 12

13 OUTGROUP SENSORS FACILITATE THE DEVELOPMENT OF TRUST TO ENCOURAGE THE ADOPTION OF THE HMN AND ENABLE EFFICIENT ACTIVITY AS FACILITATION OF COLLABORATIVE WORK WHERE EACH ACTOR IS SUPPORTED TO ACHIEVE THEIR GOALS SPECIAL SERVICES EVACUEES CAMERAS SENSORS OPERATIONS OPERATIONAL STAFF AD HOC & SECURE NETWORK USER DEVICES DECISION SUPPORT SYSTEM SEEDS OPTIMISE SELF-EFFICACY VIA COLLABORATIVE INTERACTION BETWEEN HUMANS AND MACHINES EMERGENCY SERVICES 13

14 Conclusions and further work 14

We ve demonstrated the potential of the HUMANE approach through use case engagement and subsequent

the facilitation of cross disciplinary communication or the identification of common design solutions We

Regulation needs to be understood for what it is and what it means.

15 We ve demonstrated the potential of the HUMANE approach through use case engagement and subsequent analysis But the benefit of a structured analytical approach to HMN characterisation is not confined to the facilitation of cross disciplinary communication or the identification of common design solutions We need to design networks which will allow us to capitalise on our full potential (Norman, 2010) Regulation needs to be understood for what it is and what it means. Empowerment is probably not helpful Moving forward means designing effective and successful HMNs which allow for social interaction and emergent behavioiur 15

16 Dr. J Brian Pickering, IT Innovation Centre, University of Southampton jbp@it-innovation.soton.ac.uk SNS Professional network NETWORKS OF NETWORKS 16

Grand Challenges for Systems and Services Sciences

Grand Challenges for Systems and Services Sciences Brian Monahan, David Pym, Richard Taylor, Chris Tofts, Mike Yearworth Trusted Systems Laboratory HP Laboratories Bristol HPL-2006-99 July 13, 2006* systems,