This SMART Seminar was presented on June 28, 2012. Abstract: Socio-technical systems comprise both individuals and groups of people (the social side), and information and processes (the technological side. Examples of socio-technical systems include logistics, customs, and management at an airport, time and task management of an office worker, and optimal usage of an enterprise computer network. We study one instance of a process within such a complex system: the progress of containers through customs. This process is more often an exercise in negotiation rather than a structured queuing system. Once regulatory processes involves negotiation, corruption becomes a factor. Studies by the OECD and other organizations reveal that customs corruption is not easily combated by policy changes. We suggest that simulation of potential reform policies in the maritime customs context can provide insights for decision makers. In this talk we describe work in progress towards a simulation calibrated on processes at the Port of Beirut, and argue for the applicability of agent-based modelling in the domain. This is joint work with P. Attie, R. Outa, and F. J. Srour. Bio: Neil Yorke-Smith is an Assistant Professor of Business Information and Decision Systems at the Suliman S. Olayan School of Business, American University of Beirut, and a Research Scientist at SRI International, USA. His research focuses on technologies that assist human decision making, with interests including intelligent agents, simulation and serious games, preference modelling, constraint-based reasoning, machine learning and data mining, and their real world applications. Publications and further information are available at: http://www.aub.edu.lb/~nysmith

1. Agent-Based Simulation of Socio-Technical Processes
Maritime Customs Negotiation with Corrupt Agents
Rami Outa, Paul Attie, F. Jordan Srour, Neil Yorke-Smith
nysmith@aub.edu.lb
Department of Computer Science, and Olayan School of Business
American University of Beirut
SMART | University of Wollongong | 28 June 2012
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 1 / 39

2. Outline
Socio-technical systems
Maritime customs domain
Model selection methodology
Agent-based process modelling
Simulation results
Research directions
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 2 / 39

3. Preliminaries Definitions
What is a Socio-Technical System?
Complex interactions of people, culture, information, and processes
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 3 / 39

4. Preliminaries Definitions
What is a Socio-Technical System?
Complex interactions of people, culture, information, and processes
Individuals and groups of people (the social side)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 3 / 39

5. Preliminaries Definitions
What is a Socio-Technical System?
Complex interactions of people, culture, information, and processes
Individuals and groups of people (the social side)
Information and processes (the technological side)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 3 / 39

6. Preliminaries Definitions
What is a Socio-Technical System?
Complex interactions of people, culture, information, and processes
Individuals and groups of people (the social side)
Information and processes (the technological side)
Examples:
logistics, customs, and management at an airport
time and task management of an office worker
optimal usage of an enterprise computer network
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 3 / 39

7. Preliminaries Definitions
What is Maritime Customs?
“Customs is an authority or agency in a country responsible
for collecting and safeguarding customs duties and for
controlling the flow of goods in to and out of a country.”
— Wikipedia
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 4 / 39

8. Preliminaries Definitions
What is Maritime Customs?
“Customs is an authority or agency in a country responsible
for collecting and safeguarding customs duties and for
controlling the flow of goods in to and out of a country.”
— Wikipedia
We focus on imports
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 4 / 39

9. Preliminaries Definitions
What is Maritime Customs?
“Customs is an authority or agency in a country responsible
for collecting and safeguarding customs duties and for
controlling the flow of goods in to and out of a country.”
— Wikipedia
We focus on imports
We focus on sea-based containers
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 4 / 39

10. Preliminaries Definitions
What is Maritime Customs?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 5 / 39

11. Preliminaries Definitions
What is Corruption?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 6 / 39

12. Preliminaries Definitions
What is Corruption?
Oxford English Dictionary:
▶ Moral deterioration; depravity
▶ Evil nature
▶ The perversion of integrity by bribery or favour; the use or existence of
corrupt practices
▶ The perversion of anything from an original state or purity
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 6 / 39

13. Preliminaries Definitions
What is Corruption?
Oxford English Dictionary:
▶ Moral deterioration; depravity
▶ Evil nature
▶ The perversion of integrity by bribery or favour; the use or existence of
corrupt practices
▶ The perversion of anything from an original state or purity
Not quite so easy to define …
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 6 / 39

14. Preliminaries Definitions
What is Corruption?
Oxford English Dictionary:
▶ Moral deterioration; depravity
▶ Evil nature
▶ The perversion of integrity by bribery or favour; the use or existence of
corrupt practices
▶ The perversion of anything from an original state or purity
Not quite so easy to define …
Our definition: Any deviation from the published legal process
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 6 / 39

15. Motivation Maritime Customs Process
Archetypal Published Legal Customs Process
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 7 / 39

16. Motivation Maritime Customs Process
Deviations from the Published Legal Customs Process
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 8 / 39

17. Motivation Maritime Customs Process
Deviations from the Published Legal Customs Process
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 9 / 39

18. Motivation Maritime Customs Process
Deviations from the Published Legal Customs Process
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 10 / 39

19. Motivation Maritime Customs Process
Deviations from the Published Legal Customs Process
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 11 / 39

20. Motivation Maritime Customs Process
Deviations from the Published Legal Customs Process
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 12 / 39

21. Motivation Corruption
Why Does it Matter?
Customs is major source of revenue, especially for developing
countries (OECD, 2001)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 13 / 39

22. Motivation Corruption
Why Does it Matter?
Customs is major source of revenue, especially for developing
countries (OECD, 2001)
Process deviations not easily combatted by policy changes (OECD,
2001)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 13 / 39

23. Motivation Corruption
Why Does it Matter?
Customs is major source of revenue, especially for developing
countries (OECD, 2001)
Process deviations not easily combatted by policy changes (OECD,
2001)
Policy changes can disturb business confidence — even lead to
political instability (Rose-Ackerman, 2008)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 13 / 39

24. Motivation Corruption
Why Does it Matter?
Customs is major source of revenue, especially for developing
countries (OECD, 2001)
Process deviations not easily combatted by policy changes (OECD,
2001)
Policy changes can disturb business confidence — even lead to
political instability (Rose-Ackerman, 2008)
Corruption reinforces disenfranchisement and hinders development
(Transparency International, 2009)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 13 / 39

25. Motivation Corruption
Why Does it Matter?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 14 / 39

26. Motivation Corruption
Not Just Developing Countries
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 15 / 39

27. Objectives
Research Objectives
.
1. Understand and capture processes in maritime customs
.
2 Validate model of inter-actor negotiations
3. Use simulation to examine the impact of reform policies
.
4 Contribute to best practice discussion in fitting simulation techniques
to domain problems
.
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 16 / 39

28. Objectives
Research Objectives
.
1. Understand and capture processes in maritime customs
.
2 Validate model of inter-actor negotiations
3. Use simulation to examine the impact of reform policies
.
4 Contribute to best practice discussion in fitting simulation techniques
to domain problems
.
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 16 / 39

29. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

30. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

31. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

32. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

33. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

34. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

35. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

36. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

37. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

38. Methodology
Methodology for Selecting a Modelling Paradigm
1. Identify scenario/system to be simulated, goals of simulation
.
2 Make an initial choice of modelling paradigm
3. Collect data to fuel abstraction and model-building
.
4 Review data and re-evaluate model and language choices
5. Design and build simulation
.
6 Run simulation to examine potential policy decisions
.
7 Analyze and interpret results
.
8 Collect data on fit between technique and problem
▶ possibly revise the model, or even the methodological choice
.
9 After validation, apply conclusions to policy issues in studied
scenario/system
.
10 Seek to generalize conclusions to other problems or domains
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39

39. Methodology
Four Levels of Language Decisions
Source: Terán (2004)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 18 / 39

40. Methodology
Criteria for Choice of Paradigm
1. Modelling fit: how well does the modelling paradigm suit the
(abstracted) system to be simulated?
2. Cognitive fit: how well does the modelling/theoretical paradigm suit
the thinking of the modeller?
.
3 Explanatory power: how well can the simulation developed answer
the study questions?
4. Ease of implementation: how well does the implementation
language suit the model to be implemented and the questions to be
asked?
5. Computational tractability: how readily can the simulation be
performed?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 19 / 39

41. Methodology
Criteria for Choice of Paradigm
1. Modelling fit: how well does the modelling paradigm suit the
(abstracted) system to be simulated?
2. Cognitive fit: how well does the modelling/theoretical paradigm suit
the thinking of the modeller?
.
3 Explanatory power: how well can the simulation developed answer
the study questions?
4. Ease of implementation: how well does the implementation
language suit the model to be implemented and the questions to be
asked?
5. Computational tractability: how readily can the simulation be
performed?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 19 / 39

42. Methodology
Criteria for Choice of Paradigm
1. Modelling fit: how well does the modelling paradigm suit the
(abstracted) system to be simulated?
2. Cognitive fit: how well does the modelling/theoretical paradigm suit
the thinking of the modeller?
.
3 Explanatory power: how well can the simulation developed answer
the study questions?
4. Ease of implementation: how well does the implementation
language suit the model to be implemented and the questions to be
asked?
5. Computational tractability: how readily can the simulation be
performed?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 19 / 39

43. Methodology
Criteria for Choice of Paradigm
1. Modelling fit: how well does the modelling paradigm suit the
(abstracted) system to be simulated?
2. Cognitive fit: how well does the modelling/theoretical paradigm suit
the thinking of the modeller?
.
3 Explanatory power: how well can the simulation developed answer
the study questions?
4. Ease of implementation: how well does the implementation
language suit the model to be implemented and the questions to be
asked?
5. Computational tractability: how readily can the simulation be
performed?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 19 / 39

44. Methodology
Criteria for Choice of Paradigm
1. Modelling fit: how well does the modelling paradigm suit the
(abstracted) system to be simulated?
2. Cognitive fit: how well does the modelling/theoretical paradigm suit
the thinking of the modeller?
.
3 Explanatory power: how well can the simulation developed answer
the study questions?
4. Ease of implementation: how well does the implementation
language suit the model to be implemented and the questions to be
asked?
5. Computational tractability: how readily can the simulation be
performed?
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 19 / 39

45. Modelling 1. Simulation goals
Step 1: Identify scenario and goals of simulation
.
Analysis of potential management and optimization policies
. in the maritime customs context
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 20 / 39

46. Modelling 2. Choice of paradigm
Step 2: Initial choice of modelling paradigm
Agent-Based Modelling and Multiagent-Based Simulation
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 21 / 39

47. Modelling 2. Choice of paradigm
Step 2: Initial choice of modelling paradigm
Agent-Based Modelling and Multiagent-Based Simulation
flexibility, ease of modelling
“descriptive realism …natural system boundaries” (Edmonds, 2000)
emergent behaviours; complex behaviours
scaleable/parallel computation
accessible tools
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 21 / 39

48. Modelling 2. Choice of paradigm
Step 2: Initial choice of modelling paradigm
Agent-Based Modelling and Multiagent-Based Simulation
flexibility, ease of modelling
“descriptive realism …natural system boundaries” (Edmonds, 2000)
emergent behaviours; complex behaviours
scaleable/parallel computation
accessible tools
agent-based models successful in port management (Lokuge and
Alahakoon, 2007) and optimization (Winikoff et al., 2011)
agent-based simulation successful in port stakeholder analysis
(Henesey, 2003)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 21 / 39

49. Modelling 2. Choice of paradigm
Intelligent Agents
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 22 / 39

50. Modelling 2. Choice of paradigm
Intelligent Agents
Autonomous distributed reasoning entities
Local views: no agent has global view of the system
▶ Or the system is too complex for global view to be useful
“Distributed, object oriented programming on steroids” (Srour)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 22 / 39

51. Modelling 2. Choice of paradigm
Intelligent Agents
Autonomous distributed reasoning entities
Local views: no agent has global view of the system
▶ Or the system is too complex for global view to be useful
“Distributed, object oriented programming on steroids” (Srour)
Example: centralized dispatcher for a logistics company, versus truck
drivers who accept/reject job offers as they see fit
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 22 / 39

52. Modelling 2. Choice of paradigm
Intelligent Agents
Autonomous distributed reasoning entities
Local views: no agent has global view of the system
▶ Or the system is too complex for global view to be useful
“Distributed, object oriented programming on steroids” (Srour)
Example: centralized dispatcher for a logistics company, versus truck
drivers who accept/reject job offers as they see fit
Applied in logistics, e-commerce, smart grid, cloud computing,
robotics, networking and mobile technologies
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 22 / 39

53. Modelling 2. Choice of paradigm
ABM Meta-Methodology
.
. Abstraction
1
.
2 Design
. Inference
3
.
4 Analysis
. Interpretation
5
.
6 Application
. Conclusion
7
.
Source: Edmonds (2000) and Davidsson et al. (2006)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 23 / 39

54. Modelling 3. Data Gathering
Step 3: Collect data to fuel abstraction and model-building
1. Studied the published maritime customs processes at three major
ports (PONY/NJ, Rotterdam, Beirut)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 24 / 39

55. Modelling 3. Data Gathering
Step 3: Collect data to fuel abstraction and model-building
1. Studied the published maritime customs processes at three major
ports (PONY/NJ, Rotterdam, Beirut)
.
2 Gathered anecdotal accounts from various stakeholder perspectives
associated with the Port of Beirut
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 24 / 39

56. Modelling 3. Data Gathering
Step 3: Collect data to fuel abstraction and model-building
1. Studied the published maritime customs processes at three major
ports (PONY/NJ, Rotterdam, Beirut)
.
2 Gathered anecdotal accounts from various stakeholder perspectives
associated with the Port of Beirut
3. Identified broad categories of negotiation behaviours that could not
be seen in the publications alone
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 24 / 39

57. Modelling 3. Data Gathering
1. Published Maritime Customs Processes
Source: Port Inter-Organizational Information Systems: Capabilities to Service Global
Supply Chains. P. van Baalen, R. Zuidwijk and J. van Nunen (Eds.)
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 25 / 39

58. Modelling 3. Data Gathering
1. Published Maritime Customs Processes
Nearly all ports observe similar processes
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39

59. Modelling 3. Data Gathering
1. Published Maritime Customs Processes
Nearly all ports observe similar processes
Fundamentally dependent on a match of paperwork — manifest and
declaration must match
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39

60. Modelling 3. Data Gathering
1. Published Maritime Customs Processes
Nearly all ports observe similar processes
Fundamentally dependent on a match of paperwork — manifest and
declaration must match
All ports examined have an IT system of some sort
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39

61. Modelling 3. Data Gathering
1. Published Maritime Customs Processes
Nearly all ports observe similar processes
Fundamentally dependent on a match of paperwork — manifest and
declaration must match
All ports examined have an IT system of some sort
Differences are most readily seen in import taxation schemes
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39

62. Modelling 3. Data Gathering
1. Published Maritime Customs Processes
Nearly all ports observe similar processes
Fundamentally dependent on a match of paperwork — manifest and
declaration must match
All ports examined have an IT system of some sort
Differences are most readily seen in import taxation schemes
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39

63. Modelling 3. Data Gathering
2. Example: Port of Beirut Customs Hierarchy
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 27 / 39

64. Modelling 3. Data Gathering
3. Behaviours
Non-standard practices fall into two categories:
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 28 / 39

65. Modelling 3. Data Gathering
3. Behaviours
Non-standard practices fall into two categories:
.
Relationship-based (no obvious bribe)
A family tie
A professional association
A political link
. A favour owed
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 28 / 39

66. Modelling 3. Data Gathering
3. Behaviours
Non-standard practices fall into two categories:
.
Relationship-based (no obvious bribe)
A family tie
A professional association
A political link
. A favour owed
.
Monetary-based (obvious bribing)
Cash
Gifts
. Debt waived
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 28 / 39

67. Modelling 3. Data Gathering
3. Behaviours
Non-standard practices fall into two categories:
.
Relationship-based (no obvious bribe)
A family tie
A professional association
A political link
. A favour owed
.
Monetary-based (obvious bribing)
Cash
Gifts
. Debt waived
.
Threats may also be made
.
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 28 / 39

68. Modelling 4. Re-evaluation
Step 4: Re-evaluate model and language choices
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39

69. Modelling 4. Re-evaluation
Step 4: Re-evaluate model and language choices
Equation-based modelling
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39

70. Modelling 4. Re-evaluation
Step 4: Re-evaluate model and language choices
Equation-based modelling
Monte Carlo Simulation
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39

71. Modelling 4. Re-evaluation
Step 4: Re-evaluate model and language choices
Equation-based modelling
Monte Carlo Simulation
ABSS simulation
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39

72. Modelling 4. Re-evaluation
Step 4: Re-evaluate model and language choices
Equation-based modelling
Monte Carlo Simulation
ABSS simulation
ABM and simulation with cognitive agents
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39

73. Simulation 5. Simulation Design and Implementation
Step 5: Design and build simulation
Built a simple prototype customs process
ABM using JADE
Proof of concept for two stakeholders:
customs agents and freight forwarders
Shipments analogous to rounds in a
sequential bargaining game
Negotiation options described by truth tables
No adaptation
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74. Simulation 5. Simulation Design and Implementation
Implementation Status
Implementing full ABM simulation in Jadex
Key stakeholders as BDI agents
Negotiation according to beliefs and goals
Calibrated on Port of Beirut data
No adaptation (yet)
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75. Simulation 5. Simulation Design and Implementation
Example: Documented Beirut Customs Process
18
Deliver
container for
14 inspection
-Extraction Order (Customs)
Filtering -Receipt
result Red 17
-Final extraction order
1 by interim Customs Warehouse -Extraction Order (Port) Lebanese Army
Owner of goods Clearance Agency Intelligence System
Employee -Copy of Customs treasury
6 16 Final extraction
Green receipt
By MAIL: order
6
2a Online 12 -Copy of yellow -Copy of yellow
declaration -Copy of signed document
document
yellow document -Signed delivery
-Signed delivery
-Signed delivery order
order
NOOR online order
-Payment
portal -Payment
2b Declaration Customs Dpt. of Customs Dpt. of
printout g: 7, r: 13 Treasury Inspection Affairs
Declaration Port Gates
-Extraction Order (Port) 7a appoint 7b appoint
elements & -Extraction Order (Customs)
number inspector as in scout as in
-Receipt
yellow doc yellow doc
Head of Scanning Personnel
Leading Inspector
NAJM system 5 Yellow 11 Inspection 10
Document:
-Revision of -A5 Document:
Filtering
Inspection Details of
8 Container
3b Declaration results
-Signed (again) Inspection preparation for
details Red/Green inspection
3a IM4 Folder: yellow document -Signed yellow
3c -Invoice document
-Delivery order -Packing list Container
-Bill of lading Customs Dpt. of by hand -Company registration 9 Inspection: 15 Check condition of seal
Shipping Company
Im/Export -Identity verification -Seal condition
-Address -Type of goods
-Declaration of Value Elements -Country of origin
document
4a ...
Filtering through 4b Signed
NAJM Match? delivery order
if yes
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76. Simulation 5. Simulation Design and Implementation
Actors and Agents
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77. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner
Owner’s agent
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78. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner
Owner’s agent
Freight forwarder
Shipping company
Vessel captain
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79. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner
Owner’s agent
Freight forwarder
Shipping company
Vessel captain
Clearance Agency officer
Customs Agency officer
Inspection officer
Head of Inspection
Excise officer
Head of Excise
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80. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner
Owner’s agent
Freight forwarder
Shipping company
Vessel captain
Clearance Agency officer
Customs Agency officer
Inspection officer
Head of Inspection
Excise officer
Head of Excise
Customs broker
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39

81. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner Longshoremen
Owner’s agent Customs warehouse
Freight forwarder employees
Shipping company
Vessel captain
Clearance Agency officer
Customs Agency officer
Inspection officer
Head of Inspection
Excise officer
Head of Excise
Customs broker
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39

82. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner Longshoremen
Owner’s agent Customs warehouse
Freight forwarder employees
Shipping company Port security staff
Vessel captain
Clearance Agency officer
Customs Agency officer
Inspection officer
Head of Inspection
Excise officer
Head of Excise
Customs broker
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39

83. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner Longshoremen
Owner’s agent Customs warehouse
Freight forwarder employees
Shipping company Port security staff
Vessel captain Recipient
Clearance Agency officer
Customs Agency officer
Inspection officer
Head of Inspection
Excise officer
Head of Excise
Customs broker
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39

84. Simulation 5. Simulation Design and Implementation
Actors and Agents
Owner Longshoremen
Owner’s agent Customs warehouse
Freight forwarder employees
Shipping company Port security staff
Vessel captain Recipient
Clearance Agency officer Police officer
Customs Agency officer Customs Investigation
and Audit officer
Inspection officer
Head of Inspection
Excise officer
Head of Excise
Customs broker
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39

85. Discussion
Recall: Scenario and goals of simulation
.
Analysis of potential management and optimization policies
. in the maritime customs context
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86. Discussion
Metrics for Policy Evaluation
end-to-end clearance time
time deviation from desired receipt date
cost (including bribes)
number of deviations
% of diverted revenue
number/complexity of policies
cost of policy enforcement
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87. Discussion
Modelling Considerations
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88. Discussion
Modelling Considerations
Agent negotiation patterns
▶ Who negotiates with whom, especially outside process interactions?
▶ Which decision points (negotiation opportunities) to model?
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89. Discussion
Modelling Considerations
Agent negotiation patterns
▶ Who negotiates with whom, especially outside process interactions?
▶ Which decision points (negotiation opportunities) to model?
Negotiation stopping criteria
▶ Should it be based on time? number of iterations? some value?
▶ Differs from most negotiation-related agent applications
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39

90. Discussion
Modelling Considerations
Agent negotiation patterns
▶ Who negotiates with whom, especially outside process interactions?
▶ Which decision points (negotiation opportunities) to model?
Negotiation stopping criteria
▶ Should it be based on time? number of iterations? some value?
▶ Differs from most negotiation-related agent applications
Tracking and quantifying non-monetary exchanges
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39

91. Discussion
Modelling Considerations
Agent negotiation patterns
▶ Who negotiates with whom, especially outside process interactions?
▶ Which decision points (negotiation opportunities) to model?
Negotiation stopping criteria
▶ Should it be based on time? number of iterations? some value?
▶ Differs from most negotiation-related agent applications
Tracking and quantifying non-monetary exchanges
Modelling and quantifying threats
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39

92. Discussion
Modelling Considerations
Agent negotiation patterns
▶ Who negotiates with whom, especially outside process interactions?
▶ Which decision points (negotiation opportunities) to model?
Negotiation stopping criteria
▶ Should it be based on time? number of iterations? some value?
▶ Differs from most negotiation-related agent applications
Tracking and quantifying non-monetary exchanges
Modelling and quantifying threats
Capturing social networks and relationships
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39

93. Conclusion
Summary
Methodology for simulation of socio-technical systems
Agents are suitable to model negotiation-centric processes
ABM should allow the testing of new policies
Prototype simulation design indicates promise
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39

94. Conclusion
Summary
Methodology for simulation of socio-technical systems
Agents are suitable to model negotiation-centric processes
ABM should allow the testing of new policies
Prototype simulation design indicates promise
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39

95. Conclusion
Summary
Methodology for simulation of socio-technical systems
Agents are suitable to model negotiation-centric processes
ABM should allow the testing of new policies
Prototype simulation design indicates promise
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39

96. Conclusion
Summary
Methodology for simulation of socio-technical systems
Agents are suitable to model negotiation-centric processes
ABM should allow the testing of new policies
Prototype simulation design indicates promise
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39

97. Conclusion
Current and Future Work
Implement more complex negotiation behaviours
Analyze behavioural results and policy implications
Expand to include several freight types and exports
Contrast policies and structure with other ports
Continue with study of socio-technical systems
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98. Conclusion
Questions?
Rami Outa Paul Attie F. Jordan Srour Neil Yorke-Smith
rhe16 pa07 fs49 nysmith
Thanks to Teressa Eid and Hassan Harb. We thank the participants of the Agent-based
Decision Support in Auctions and Negotiations session at the INFORMS Annual
Meeting 2010, and the reviewers of the Agent-based Modeling for Policy Engineering
workshop at AAMAS 2011. The authors were partially supported by University Research
Board grants A88813 and A288810 from the American University of Beirut.
©2012 N. Yorke-Smith
Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 39 / 39