air traffic management: it´s all about people

Air Traffic Management: it´s all about people

Victor Rafael R. Celestino

Researcher Trajectory

• Victor Rafael R. Celestino• 54 yrs, married, 2 children

Aviator´s offspring, first flight on Beech C45 at 6 mo old (1962) and first LongHaul trip on B25, Rio – Fortaleza in 20 hours (1966)

In flight testing, performed first maneuvers on T27 (1985), piloted (hands on stick) T38 Talon, breaking sound barrier, and operated from aircraft carrier with S-2/P-16

Diversified experience with fixed and rotaty wings, on dozens of models, including simulators, build a career in aviation

Presently Technical Consultant at the Brazilian Airlines Association (ABEAR): www.abear.com.br; and PhD student at UCB (Psychology)

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Summary

IntroductionATM: it´s all about people Fatigue at work among pilots and ATC Human Behavior and Decision Making Game Theory vs Dynamic Systems Final Statements and Future DirectionsReferences

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• Air Traffic Management magazine article after major failure in Voice Control System (VCS) at NATS Swanwick and crash of the Asiana Airlines 777 in San Francisco: https://www.youtube.com/watch?v=HhZFBJhXMXU

Introduction

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Fatigue on the News

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• ABRAPAC: Sleepiness and Fatige (12/19/2013): https://www.youtube.com/watch?v=fgMHiY0jA9U

• NBC NEWS (08/11/2015): http://www.nbcnews.com/health/health-news/air-controller-study-shows-theyre-dangerously-sleepy-n407256

• G1 – BOM DIA BRASIL (08/12/2015): http://g1.globo.com/bom-dia-brasil/noticia/2015/08/controlador-de-voo-dorme-pouco-e-poe-passageiros-em-perigo-diz-nasa.html

• Fatigue has been on the NTSB´s “most wanted list” since 1990 and remains a topic of active investigation. Major factor are: shift work; task complexity and workload; non-work-related factors (Nealley and Gawron, 2015).

ATM: It´s all about people

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• FAA contracted NASA to perform a comprehensive field study in 2010. FAA took action on risks found (Orasanu et al, 2012).


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• FAA contracted NASA to perform a comprehensive field study in 2010. FAA took action on risks found (Orasanu et al, 2012).


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Fatigue at Work

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Fatigue• Fatigue ≠ Sleepiness (Akerstedt and Wright Jr, 2009);• My PhD research question: how the (self-) perception measure of

fatigue at work among pilots can reflect on social perception of civil aviation as safer?

• Cross-sectional observational study with secondary data from a research supported by ABRAPAC. Among 1,234 answers from survey, sample was 1,193 male pilots.

• For the analysis of fatigue, a multinomial regression was performed with structural equation model. Statistical significance in explaining fatigue suggests its systemic impact on the life of these professionals (Celestino et al, 2015).

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Fatigue: Systems Approach• Similarities of behavior in decision making process at

interpersonal relationships level, among couples in marital conflict, NASA´s managers and engineers in technical evaluation of space shuttle components, and air traffic controllers interpreting information on radar screens (Vaughan, 2002).

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Human Behavior and Decision Making• Theoretical perspectives on human behavior (Hutchison and

Charlesworth, 2011):1. Rational choice perspective: human behavior as based on self-interest and rational

choices about effective ways to accomplish goals.2. Systems perspective: human behavior as the outcome of reciprocal interactions of

persons operating within linked social systems.3. Conflict perspective: draws attention to conflict, inequality, dominance, and

oppression in social life.4. Developmental perspective: how human behavior unfolds across the life course.5. Humanistic perspective: emphasizes the individual’s inherent value, freedom of

action, and search for meaning.6. Psychodynamic perspective: how internal processes such as needs, drives, and

emotions motivate human behavior.7. Social behavioral perspective: human behavior is learned as individuals interact with

their environments.8. Social constructionist perspective: how people learn, through their interactions with

each other, to understand the world and their place in it.

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Human Behavior and Decision Making• Behavioral game theoy models (Camerer and Ho, 2015):

1. Cognitive hierarchy (CH): captures players’ beliefs about other players, using parameter to describe the average step of strategic thinking (level-k LK models). Predict one-shot games or initial conditions in repeated game.

2. Noisy optimization (Quantal response equilibrium – QRE): players are allowed to make small mistakes but they always have accurate beliefs about what other players will do (beliefs are noisy).

3. Learning (Experience-Weighted Attraction Learning – EWA): predicts time path of individual behavior as function of initial conditions (CH model). Each EWA parameter has natural interpretation: foregone payoffs (psychological regret or opportunity cost); forgetting (retire old information); and attraction growth.

4. Extension of learning models: include sophistication (understanding how others learn) as well as strategic teaching, and nonequilibrium reputation-building. Allows the population to have sophisticated players who actively influence adaptive players’ learning paths to benefit themselves.

5. Models of how social preferences map monetary payoffs (controlled in an experiment) into utilities and behavior (departures from pure self interest).

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Game Theory vs Dynamic Systems• DA-CDM model: Deferred Acceptance Collaborative Decision Making at

two-sided market (flights and slots), assigning flights to slots based on one-to-one relationship, respecting preferences of each allocation. Guaranteed to stable matching of flights and slots (De Arruda et al, 2015).

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Game Theory vs Dynamic Systems

• Theorem 1: there always exist a stable set of marriages, proven by iterative procedure (Gale e Shapley, 1962). Author acknowledges in conclusion: “in discussing the marriage problem, we abandoned reality altogether and enter the world of mathematical make-believe”.

• Exemplification at TED – Mathematics of Love by Hannah Fry: https://www.youtube.com/watch?v=yFVXsjVdvmY

• Critics: “no stable matching procedure exists that make it a dominant strategy” and a revised theory for two-sided matching markets, including marriage problem (Roth, 1985)

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Game Theory vs Dynamic Systems• In social sciences, classical game theory badly predicts behavior. Even in

behavioral game theory, there are two problems (Lucas et al, 2015): 1. For a chooser, contemplating the range of possible deviations, as there are many

dozens, actually makes it exponentially harder to figure out a path to an outcome. This makes the theoretical models useless for modeling human thought or human behavior in general;

2. Modeling deviations is helpful only if the deviations are consistent, so that scientists (and indeed decision-makers) can make predictions about future choices on the basis of past choices. But the deviations are not consistent. In general, deviations from classical models are not consistent for any individual from one task to the next or between individuals for the same task. In addition, people’s beliefs are in general not consistent with their choices.

• Future of game theory would come from a grounding in cognitive science, or more generally, in the analysis of how the cognitively modern human mind works, what its basic mental operations are, and how they are deployed in situations.

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Game Theory vs Dynamic Systems• Lines of research in cognitive science not considered in economics (Lucas et

al, 2015):1. Variation across domains and situations: ways in which human thought varies

across different domains and situations2. Learning: various mental operations involved in learning, and how human thought

and action depend upon the highly flexible and powerful learning for which human beings are equipped.

3. Complexity and nonlinearity: actors are engaged in simultaneous games that overlap. In life, any action is usually a move in many different games. Strategies to maximize expected utility over all these games are typically nonlinear

4. Adaptive behavior: people are adaptive. First and strongest disposition is often not to play the game but to reinvent it, change it.

5. Construal. our rich capacities for differing construals of the same given material. 6. What’s up? Actors must operate in general without knowing what game they are in,

and attempt to influence other actors' thoughts about the game being played.7. Identity: people construct an identity for themselves, and carry it and vary it

appropriately from situation to situation

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Game Theory vs Dynamic Systems• General systems theory of marriage (Gottman et al, 2002): mathematical

approach for modeling prediction of divorce or marital stability from marital interaction using nonlinear difference equations (general for modeling social interaction):

– First Question: What are the Steady States of the System?– Second Question: Which Steady States are Stable?– Describing the Behavior of the Model: flow chart for dynamic modeling– Writing the Equations of Marital Interaction: mathematical form of the influence functions

• The Full Equations:

Wt +1 = IHW(Ht) + r1 * Wt + a Ht +1 = IWH(Wt +1) + r2 * Ht + b

– Influence functions IAB (At): influence of person A’s state at turn t on person B’s state. Asymmetry in indexes is due to assuming that wife speaks first. The problem is estimation of our four parameters, r1, a , r2, and b, and the empirical determination of the two unknown influence functions.

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Game Theory vs Dynamic Systems

• Implications of the General systems theory of marriage (Gottman et al, 2002): compared to happy, stable marriages, in marriages headed for divorce:

– there is more emotional inertia;– even before being influenced, the uninfluenced set point is more negative;– when interaction begins, the couple influences one another to become even

more negative, rather than more positive;– over time, as these negative interactions continue and become characteristic of

the marriage, the couple may catastrophically lose its positive stable steady state.

• This dream of a mathematics for social relationships is not new. The work is reminiscent of Isaac Asimov’s science fiction classic series of books, called the Foundation series. In that series of books, a fictional mathematician named Hari Seldon creates a set of equations for predicting the future of the entire human species, a new branch of study he calls “psychohistory.”

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Final Statements and Future Directions

• ATM: it is all about people. Even with the continued technological development, the human factor in the loop must be considered if we seek good models.

• Game theory need to incorporate cognitive science in order to remain a viable modeling tool.

• Dynamic systems are the frontier. Lack of data can be handled by new tools for Qualitative Reasoning and Qualitative Simulation, such as Garp3 and DynaLearn (Liem, 2013).

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passageiros. Conexão SIPAER, v. 6, n. 1, p. 18-28, 2015. • DE ARRUDA, A. C.; WEIGANG, L.; MILEA, V. A new Airport Collaborative Decision Making algorithm based on Deferred Acceptance in a two-

sided market. Expert Systems with Applications, v. 42, n. 7, p. 3539-3550, 2015. • GALE, D.; SHAPLEY, L. S. College admissions and the stability of marriage. American mathematical monthly, p. 9-15, 1962. • GOTTMAN, J.; SWANSON, C.; SWANSON, K. A general systems theory of marriage: Nonlinear difference equation modeling of marital

interaction. Personality and social psychology review, v. 6, n. 4, p. 326-340, 2002. • HUTCHISON, E. D.; CHARLESWORTH, L. W. Theoretical perspectives on human behavior. In: HUTCHISON, E. D. (Ed.). Dimensions of human

behavior: Person and environment. 4a. Thousand Oaks: SAGE, 2011. p.34-69. • LIEM, J. Supporting Conceptual Modeling of Dynamic Systems: A Knowledge Engineering Perspective on Qualitative Reasoning . 2013.

(Doctor). Faculteit der Natuurwetenschappen, Wiskunde en Informatica, Universiteit van Amsterdam, Amsterdam.• LUCAS, G. M.; MCCUBBINS, M. D.; TURNER, M. Against Game Theory. Emerging Trends in the Social and Behavioral Sciences: An

Interdisciplinary, Searchable, and Linkable Resource, 2015. • NEALLEY, M. A.; GAWRON, V. J. The Effect of Fatigue on Air Traffic Controllers. The International Journal of Aviation Psychology, v. 25, n. 1, p.

14-47, 2015. • ORASANU, J. et al. Evaluating the Effectiveness of Schedule Changes for Air Traffic Service (ATS) Providers: Controller Alertness and Fatigue

Monitoring Study. Federal Aviation Administration. 2012. (DOT/FAA/HFD-13/001)• ROTH, A. E. The college admissions problem is not equivalent to the marriage problem. Journal of economic Theory, v. 36, n. 2, p. 277-288,

1985. • VAUGHAN, D. Signals and interpretive work: The role of culture in a theory of practical action. In: CERULLO, K. A. (Ed.). Culture in mind:

Toward a sociology of culture and cognition. New York: Routledge, 2002. p.28-54.

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