Agent-Based Simulation of Socio-Technical Processes                Maritime Customs Negotiation with Corrupt Agents       ...
Outline        Socio-technical systems        Maritime customs domain        Model selection methodology        Agent-base...
Preliminaries   DefinitionsWhat is a Socio-Technical System? Complex interactions of people, culture, information, and proc...
Preliminaries   DefinitionsWhat is a Socio-Technical System? Complex interactions of people, culture, information, and proc...
Preliminaries   DefinitionsWhat is a Socio-Technical System? Complex interactions of people, culture, information, and proc...
Preliminaries   DefinitionsWhat is a Socio-Technical System? Complex interactions of people, culture, information, and proc...
Preliminaries   DefinitionsWhat is Maritime Customs?            “Customs is an authority or agency in a country responsible...
Preliminaries   DefinitionsWhat is Maritime Customs?            “Customs is an authority or agency in a country responsible...
Preliminaries   DefinitionsWhat is Maritime Customs?            “Customs is an authority or agency in a country responsible...
Preliminaries   DefinitionsWhat is Maritime Customs?Outa, Attie, Srour, Yorke-Smith (AUB)             SMART              28...
Preliminaries   DefinitionsWhat is Corruption?Outa, Attie, Srour, Yorke-Smith (AUB)             SMART              28 June ...
Preliminaries   DefinitionsWhat is Corruption?        Oxford English Dictionary:            ▶   Moral deterioration; deprav...
Preliminaries   DefinitionsWhat is Corruption?        Oxford English Dictionary:            ▶   Moral deterioration; deprav...
Preliminaries   DefinitionsWhat is Corruption?        Oxford English Dictionary:            ▶   Moral deterioration; deprav...
Motivation   Maritime Customs ProcessArchetypal Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (AUB)      ...
Motivation   Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (A...
Motivation   Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (A...
Motivation   Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (A...
Motivation   Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (A...
Motivation   Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (A...
Motivation   CorruptionWhy Does it Matter?        Customs is major source of revenue, especially for developing        cou...
Motivation   CorruptionWhy Does it Matter?        Customs is major source of revenue, especially for developing        cou...
Motivation   CorruptionWhy Does it Matter?        Customs is major source of revenue, especially for developing        cou...
Motivation   CorruptionWhy Does it Matter?        Customs is major source of revenue, especially for developing        cou...
Motivation   CorruptionWhy Does it Matter?Outa, Attie, Srour, Yorke-Smith (AUB)           SMART             28 June 2012  ...
Motivation   CorruptionNot Just Developing CountriesOuta, Attie, Srour, Yorke-Smith (AUB)           SMART             28 J...
ObjectivesResearch Objectives .     1. Understand and capture processes in maritime customs      .     2 Validate model of...
ObjectivesResearch Objectives .     1. Understand and capture processes in maritime customs      .     2 Validate model of...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyMethodology for Selecting a Modelling Paradigm    1. Identify scenario/system to be simulated, goals of simulat...
MethodologyFour Levels of Language Decisions Source: Terán (2004)Outa, Attie, Srour, Yorke-Smith (AUB)            SMART   ...
MethodologyCriteria for Choice of Paradigm    1. Modelling fit: how well does the modelling paradigm suit the       (abstra...
MethodologyCriteria for Choice of Paradigm    1. Modelling fit: how well does the modelling paradigm suit the       (abstra...
MethodologyCriteria for Choice of Paradigm    1. Modelling fit: how well does the modelling paradigm suit the       (abstra...
MethodologyCriteria for Choice of Paradigm    1. Modelling fit: how well does the modelling paradigm suit the       (abstra...
MethodologyCriteria for Choice of Paradigm    1. Modelling fit: how well does the modelling paradigm suit the       (abstra...
Modelling   1. Simulation goalsStep 1: Identify scenario and goals of simulation .             Analysis of potential manag...
Modelling   2. Choice of paradigmStep 2: Initial choice of modelling paradigm Agent-Based Modelling and Multiagent-Based S...
Modelling   2. Choice of paradigmStep 2: Initial choice of modelling paradigm Agent-Based Modelling and Multiagent-Based S...
Modelling   2. Choice of paradigmStep 2: Initial choice of modelling paradigm Agent-Based Modelling and Multiagent-Based S...
Modelling   2. Choice of paradigmIntelligent AgentsOuta, Attie, Srour, Yorke-Smith (AUB)          SMART                   ...
Modelling   2. Choice of paradigmIntelligent Agents        Autonomous distributed reasoning entities        Local views: n...
Modelling   2. Choice of paradigmIntelligent Agents        Autonomous distributed reasoning entities        Local views: n...
Modelling   2. Choice of paradigmIntelligent Agents        Autonomous distributed reasoning entities        Local views: n...
Modelling   2. Choice of paradigmABM Meta-Methodology                                        .                            ...
Modelling   3. Data GatheringStep 3: Collect data to fuel abstraction and model-building    1. Studied the published marit...
Modelling   3. Data GatheringStep 3: Collect data to fuel abstraction and model-building    1. Studied the published marit...
Modelling   3. Data GatheringStep 3: Collect data to fuel abstraction and model-building    1. Studied the published marit...
Modelling   3. Data Gathering1. Published Maritime Customs Processes Source: Port Inter-Organizational Information Systems...
Modelling   3. Data Gathering1. Published Maritime Customs Processes        Nearly all ports observe similar processesOuta...
Modelling   3. Data Gathering1. Published Maritime Customs Processes        Nearly all ports observe similar processes    ...
Modelling   3. Data Gathering1. Published Maritime Customs Processes        Nearly all ports observe similar processes    ...
Modelling   3. Data Gathering1. Published Maritime Customs Processes        Nearly all ports observe similar processes    ...
Modelling   3. Data Gathering1. Published Maritime Customs Processes        Nearly all ports observe similar processes    ...
Modelling   3. Data Gathering2. Example: Port of Beirut Customs HierarchyOuta, Attie, Srour, Yorke-Smith (AUB)          SM...
Modelling   3. Data Gathering3. Behaviours Non-standard practices fall into two categories:Outa, Attie, Srour, Yorke-Smith...
Modelling   3. Data Gathering3. Behaviours Non-standard practices fall into two categories: . Relationship-based (no obvio...
Modelling   3. Data Gathering3. Behaviours Non-standard practices fall into two categories: . Relationship-based (no obvio...
Modelling   3. Data Gathering3. Behaviours Non-standard practices fall into two categories: . Relationship-based (no obvio...
Modelling   4. Re-evaluationStep 4: Re-evaluate model and language choicesOuta, Attie, Srour, Yorke-Smith (AUB)          S...
Modelling   4. Re-evaluationStep 4: Re-evaluate model and language choices        Equation-based modellingOuta, Attie, Sro...
Modelling   4. Re-evaluationStep 4: Re-evaluate model and language choices        Equation-based modelling        Monte Ca...
Modelling   4. Re-evaluationStep 4: Re-evaluate model and language choices        Equation-based modelling        Monte Ca...
Modelling   4. Re-evaluationStep 4: Re-evaluate model and language choices        Equation-based modelling        Monte Ca...
Simulation   5. Simulation Design and ImplementationStep 5: Design and build simulation          Built a simple prototype ...
Simulation   5. Simulation Design and ImplementationImplementation Status        Implementing full ABM simulation in Jadex...
Simulation                               5. Simulation Design and ImplementationExample: Documented Beirut Customs Process...
Simulation   5. Simulation Design and ImplementationActors and AgentsOuta, Attie, Srour, Yorke-Smith (AUB)           SMART...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner              Owner’s agentOuta, A...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner              Owner’s agent       ...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner              Owner’s agent       ...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner              Owner’s agent       ...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner                                  ...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner                                  ...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner                                  ...
Simulation   5. Simulation Design and ImplementationActors and Agents              Owner                                  ...
DiscussionRecall: Scenario and goals of simulation .             Analysis of potential management and optimization policie...
DiscussionMetrics for Policy Evaluation        end-to-end clearance time        time deviation from desired receipt date  ...
DiscussionModelling ConsiderationsOuta, Attie, Srour, Yorke-Smith (AUB)          SMART   28 June 2012   36 / 39
DiscussionModelling Considerations        Agent negotiation patterns            ▶   Who negotiates with whom, especially o...
DiscussionModelling Considerations        Agent negotiation patterns            ▶   Who negotiates with whom, especially o...
DiscussionModelling Considerations        Agent negotiation patterns            ▶   Who negotiates with whom, especially o...
DiscussionModelling Considerations        Agent negotiation patterns            ▶   Who negotiates with whom, especially o...
DiscussionModelling Considerations        Agent negotiation patterns            ▶   Who negotiates with whom, especially o...
ConclusionSummary        Methodology for simulation of socio-technical systems        Agents are suitable to model negotia...
ConclusionSummary        Methodology for simulation of socio-technical systems        Agents are suitable to model negotia...
ConclusionSummary        Methodology for simulation of socio-technical systems        Agents are suitable to model negotia...
ConclusionSummary        Methodology for simulation of socio-technical systems        Agents are suitable to model negotia...
ConclusionCurrent and Future Work        Implement more complex negotiation behaviours        Analyze behavioural results ...
ConclusionQuestions? Rami Outa                     Paul Attie                F. Jordan Srour   Neil Yorke-Smith rhe16     ...
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SMART Seminar Series: ‘Agent-Based Simulation of Socio-Technical Processes: Maritime Customs Negotiation With Corrupt Agents

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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

Published in: Education, Business, Technology
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Transcript of "SMART Seminar Series: ‘Agent-Based Simulation of Socio-Technical Processes: Maritime Customs Negotiation With Corrupt Agents"

  1. 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 2012Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 1 / 39
  2. 2. Outline Socio-technical systems Maritime customs domain Model selection methodology Agent-based process modelling Simulation results Research directionsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 2 / 39
  3. 3. Preliminaries DefinitionsWhat is a Socio-Technical System? Complex interactions of people, culture, information, and processesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 3 / 39
  4. 4. Preliminaries DefinitionsWhat 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. 5. Preliminaries DefinitionsWhat 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. 6. Preliminaries DefinitionsWhat 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 networkOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 3 / 39
  7. 7. Preliminaries DefinitionsWhat 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.” — WikipediaOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 4 / 39
  8. 8. Preliminaries DefinitionsWhat 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 importsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 4 / 39
  9. 9. Preliminaries DefinitionsWhat 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 containersOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 4 / 39
  10. 10. Preliminaries DefinitionsWhat is Maritime Customs?Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 5 / 39
  11. 11. Preliminaries DefinitionsWhat is Corruption?Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 6 / 39
  12. 12. Preliminaries DefinitionsWhat 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 purityOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 6 / 39
  13. 13. Preliminaries DefinitionsWhat 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. 14. Preliminaries DefinitionsWhat 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 processOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 6 / 39
  15. 15. Motivation Maritime Customs ProcessArchetypal Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 7 / 39
  16. 16. Motivation Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 8 / 39
  17. 17. Motivation Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 9 / 39
  18. 18. Motivation Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 10 / 39
  19. 19. Motivation Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 11 / 39
  20. 20. Motivation Maritime Customs ProcessDeviations from the Published Legal Customs ProcessOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 12 / 39
  21. 21. Motivation CorruptionWhy 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. 22. Motivation CorruptionWhy 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. 23. Motivation CorruptionWhy 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. 24. Motivation CorruptionWhy 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. 25. Motivation CorruptionWhy Does it Matter?Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 14 / 39
  26. 26. Motivation CorruptionNot Just Developing CountriesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 15 / 39
  27. 27. ObjectivesResearch 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. 28. ObjectivesResearch 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. 29. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  30. 30. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  31. 31. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  32. 32. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  33. 33. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  34. 34. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  35. 35. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  36. 36. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  37. 37. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  38. 38. MethodologyMethodology 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 domainsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 17 / 39
  39. 39. MethodologyFour Levels of Language Decisions Source: Terán (2004)Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 18 / 39
  40. 40. MethodologyCriteria 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. 41. MethodologyCriteria 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. 42. MethodologyCriteria 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. 43. MethodologyCriteria 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. 44. MethodologyCriteria 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. 45. Modelling 1. Simulation goalsStep 1: Identify scenario and goals of simulation . Analysis of potential management and optimization policies . in the maritime customs contextOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 20 / 39
  46. 46. Modelling 2. Choice of paradigmStep 2: Initial choice of modelling paradigm Agent-Based Modelling and Multiagent-Based SimulationOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 21 / 39
  47. 47. Modelling 2. Choice of paradigmStep 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 toolsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 21 / 39
  48. 48. Modelling 2. Choice of paradigmStep 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. 49. Modelling 2. Choice of paradigmIntelligent AgentsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 22 / 39
  50. 50. Modelling 2. Choice of paradigmIntelligent 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. 51. Modelling 2. Choice of paradigmIntelligent 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 fitOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 22 / 39
  52. 52. Modelling 2. Choice of paradigmIntelligent 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 technologiesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 22 / 39
  53. 53. Modelling 2. Choice of paradigmABM 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. 54. Modelling 3. Data GatheringStep 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. 55. Modelling 3. Data GatheringStep 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 BeirutOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 24 / 39
  56. 56. Modelling 3. Data GatheringStep 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 aloneOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 24 / 39
  57. 57. Modelling 3. Data Gathering1. 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. 58. Modelling 3. Data Gathering1. Published Maritime Customs Processes Nearly all ports observe similar processesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39
  59. 59. Modelling 3. Data Gathering1. Published Maritime Customs Processes Nearly all ports observe similar processes Fundamentally dependent on a match of paperwork — manifest and declaration must matchOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39
  60. 60. Modelling 3. Data Gathering1. 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 sortOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39
  61. 61. Modelling 3. Data Gathering1. 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 schemesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39
  62. 62. Modelling 3. Data Gathering1. 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 schemesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 26 / 39
  63. 63. Modelling 3. Data Gathering2. Example: Port of Beirut Customs HierarchyOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 27 / 39
  64. 64. Modelling 3. Data Gathering3. Behaviours Non-standard practices fall into two categories:Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 28 / 39
  65. 65. Modelling 3. Data Gathering3. Behaviours Non-standard practices fall into two categories: . Relationship-based (no obvious bribe) A family tie A professional association A political link . A favour owedOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 28 / 39
  66. 66. Modelling 3. Data Gathering3. 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 waivedOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 28 / 39
  67. 67. Modelling 3. Data Gathering3. 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. 68. Modelling 4. Re-evaluationStep 4: Re-evaluate model and language choicesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39
  69. 69. Modelling 4. Re-evaluationStep 4: Re-evaluate model and language choices Equation-based modellingOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39
  70. 70. Modelling 4. Re-evaluationStep 4: Re-evaluate model and language choices Equation-based modelling Monte Carlo SimulationOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39
  71. 71. Modelling 4. Re-evaluationStep 4: Re-evaluate model and language choices Equation-based modelling Monte Carlo Simulation ABSS simulationOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39
  72. 72. Modelling 4. Re-evaluationStep 4: Re-evaluate model and language choices Equation-based modelling Monte Carlo Simulation ABSS simulation ABM and simulation with cognitive agentsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 29 / 39
  73. 73. Simulation 5. Simulation Design and ImplementationStep 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 adaptationOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 30 / 39
  74. 74. Simulation 5. Simulation Design and ImplementationImplementation 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)Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 31 / 39
  75. 75. Simulation 5. Simulation Design and ImplementationExample: 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 sealShipping 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 yesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 32 / 39
  76. 76. Simulation 5. Simulation Design and ImplementationActors and AgentsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  77. 77. Simulation 5. Simulation Design and ImplementationActors and Agents Owner Owner’s agentOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  78. 78. Simulation 5. Simulation Design and ImplementationActors and Agents Owner Owner’s agent Freight forwarder Shipping company Vessel captainOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  79. 79. Simulation 5. Simulation Design and ImplementationActors 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 ExciseOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  80. 80. Simulation 5. Simulation Design and ImplementationActors 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 brokerOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  81. 81. Simulation 5. Simulation Design and ImplementationActors 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 brokerOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  82. 82. Simulation 5. Simulation Design and ImplementationActors 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 brokerOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  83. 83. Simulation 5. Simulation Design and ImplementationActors 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 brokerOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  84. 84. Simulation 5. Simulation Design and ImplementationActors 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 brokerOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 33 / 39
  85. 85. DiscussionRecall: Scenario and goals of simulation . Analysis of potential management and optimization policies . in the maritime customs contextOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 34 / 39
  86. 86. DiscussionMetrics 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 enforcementOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 35 / 39
  87. 87. DiscussionModelling ConsiderationsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39
  88. 88. DiscussionModelling Considerations Agent negotiation patterns ▶ Who negotiates with whom, especially outside process interactions? ▶ Which decision points (negotiation opportunities) to model?Outa, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39
  89. 89. DiscussionModelling 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 applicationsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39
  90. 90. DiscussionModelling 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 exchangesOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39
  91. 91. DiscussionModelling 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 threatsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39
  92. 92. DiscussionModelling 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 relationshipsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 36 / 39
  93. 93. ConclusionSummary 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 promiseOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39
  94. 94. ConclusionSummary 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 promiseOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39
  95. 95. ConclusionSummary 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 promiseOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39
  96. 96. ConclusionSummary 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 promiseOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 37 / 39
  97. 97. ConclusionCurrent 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 systemsOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 38 / 39
  98. 98. ConclusionQuestions? 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-SmithOuta, Attie, Srour, Yorke-Smith (AUB) SMART 28 June 2012 39 / 39

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