Simulated Tabletop Exercise for Risk Management - Anti Bio-terrorism Scenario Simulated Tabletop Exercise Page 1 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 ABSTRACT In this paper we focus on the concept of simulation sup- ported tabletop exercise and its application to risk manage- ment for bio-terrorism by smallpox. For the purpose we have developed the simulation model of the infection proc- ess by smallpox on a virtual city. The simulation supported tabletop exercise has designed on our simulation model for risk management by evaluating several types of policy sce- narios against bio-terrorism by smallpox. The simulation supported tabletop exercise was executed by some profes- sionals against bio-terrorism at Global Security Center, Keio University. We clarify the model structure of the simu- lation against bio-terrorism and its countermeasure poli- cies. We also show the result executed at Global Security Center. MODEL FOR SIMULATION EPIDEMIOLOGY SIMULATION EPIDEMIOLOGY The development of a simulation epidemiology model that enables various specialists in fields such as urban de- velopment and infectious diseases to easily get involved in modeling of emergent and re-emergent infectious diseases like smallpox and influenza and to conduct tabletop exer- cises based on that model has an important role to play in the evaluation of countermeasures to deal with the threat of these diseases. The epidemiology model is equipped with a large number of parameters. By combining these parameters, a wide variety of scenarios can be created. These can be roughly divided into three types: 1) Urban scenario; 2) Pathological scenario; and 3) Policy scenario. By conduct- ing simulations based on these created scenarios, it is possi- ble to effectively assess policy proposals. Thus, the simulation epidemiology model makes it possible to experience and assess infectious disease prepar- edness – something that is impossible to do in the real world (non-virtually) – through computer simulations. The simulation model is composed of three modules: 1) Pathological transition module; 2) Urban and population structure module; and 3) Infection process module. Infec- tion prevention countermeasures are introduced to the model at various levels, and assessments are conducted. PATHOLOGICAL TRANSITION MODEL In order to describe a pathological transition of emerg- ing or re-emerging infectious diseases such as smallpox and influenza, we categorized infection levels into several states, and utilized a model that is defined by 1) the number of days spent that each state endures, and 2) the probability of transition between states. Figure 2 shows the general SIMULATED TABLETOP EXERCISE FOR RISK MANAGEMENT - ANTI BIO-TERORISM MULTI SCENARIO SIMULATED TABLETOP EXERCISE Hiroshi Deguchi Tokyo Institute of Technology deguchi@dis.titech.ac.jp Tomoya Saito Keio University saitots@biopreparedness.jp Manabu Ichikawa Tokyo Institute of Technology ichikawa@dis.titech.ac.jp Hideki Tanuma Tokyo Institute of Technology tanuma@cabsss.titech.ac.jp mailto:deguchi@dis.titech.ac.jp mailto:saitots@biopreparedness.jp mailto:deguchi@dis.titech.ac.jp Page 2 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 framework of the model, and Figure 3 and 4 show the pathological transition models in the cases of smallpox and influenza, respectively. Circled numbers denote pathology levels. 1) Level 0 denotes the stage before infection; 2) level 1 denotes a state of infection before the ap- pearance of symptoms, before the virus is elimi- nated; 3) level 2 denotes a state of infection after the ap- pearance of symptoms, after the virus is elimi- nated; 4) sequence 2m→3m→5→0i shows a transition of states expressing an unapparent infection; 5) sequence 3→5→0i shows a general transition of states from infection to recovery; 6) level 3s denotes that symptoms are more serious than at level 3; 7) sequence 3s→4c→D shows a transition of states from serious symptoms, to life-threatening condi- tion, and to death; 8) sequence 3s→4m→5→0i shows a transition of states from serious symptoms to recovery; 9) level 0i denotes a state of recovery and acquisition of immunity. The lines between pathological states represent transi- tion routes, and numbers and letters over the lines express transition probability. The items circled by the dotted lines express the dura- tion of each pathological state. The process of pathological transition also varies ac- cording to medical treatment patterns. However, here we have omitted any variation of transition processes due to medical treatment. URBAN AND POPULATION STRUCTURE MODEL Population Composition Model. The population is categorized into five generations (baby, schoolchild, stu- dent, young, middle, and old). We assumed that the “middle” and “old” generations make up 30% of the popu- lation, all of which has permanent immunity, while the remaining generations (baby, schoolchild, student, young) make up 70% of the population and do not have immunity. Table 1 summarizes this assumed population composition model. City Structure and Human Activity Model. The urban structure in this model is not a replica of an actual city. It has been created as a generic city model by extract- ing factors related to infection. These factors related to in- fection are as follows. 1) Family composition ratio: The proportion of fami- lies having a particular number of members 2) School enrollment rate: Proportion of “schoolchild” and “student” generations going to school 3) Employment rate: Proportion of “young” and “middle” generations commuting to a workplace. Does not include the self-employed working from home or farmers. “Young” people commuting to university are included here. 4) City size: Assumed size of city in terms of popula- tion. Here we assumed a city of 10,000 people. 5) Number and size of offices: Number of offices and their size (small, medium, large) 6) Number of schools: Number of elementary, junior high and high schools The characteristics of the human activity model are as follows. 1) Workers and students commute to their work- places and schools via transportation channels (such as train carriages and buses of certain ca- pacities). 2) Infected persons are taken to hospital, depending on their condition. Figure 5,6 and 7 show a city structure model, a family structure model and a human activity model respectively. Generation Age Rate Immunity baby 0-5 10 % absence schoolchild 6-12 20 % student 13-18 20 % young 19-34 20 % middle 35-59 20 % presence old 60 10 % Table 1 Population Composition Model Page 3 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Note that infection in a hospital is not included within the scope of this model. INFECTION PROCESS MODEL In this model, we assume the two way of virus con- tamination process. The one is “person to place” contami- nation (Figure 8) and the other is “place to person” con- tamination (Figure 9). In other words, we do not discuss direct person-to-person infection. Several contamination and infection protection filters exist among the infection processes. In this model, we used the following four types of filters (Figure10, 11) and vacci- nation (Figure 12). (1) Discharge (Excretion) suppression filter: We as- sume that “filters” such as masks are used as a measure to suppress contamination of places by infected persons. (2) Decontamination filters for places: We assume that “decontamination filters” such as disinfectants are used to attenuate the contamination of infected places over time. (3) Infection prevention filter: As means of suppress- ing infection from places to people, we assume that “prevention filters” such as the spatial (population) density of places and individual safe- guards such as N95 masks are used. Note that the lower the spatial (population) density of a place, the lower the risk of infection. One possible form of spatial density countermeasure is to promote more flexible working hours so that people can commute at staggered times, in order to ease the congestion of public transportation. Another way is to change the lay- out of classrooms to reduce proximity between students. (4) Disinfection filter for individuals: We assume that “disinfection filters” are used to reduce the degree of infection of people over time. Furthermore, the contamination of individuals will ultimately be determined by the risk of infection to indi- viduals. This risk depends on “antibody titer,” which is determined by vaccination (Figure 12). In addition to medical countermeasures such as vaccination, these several social filters (Figure 11) play a major role in protection from infection. In this epidemiology model, infection coun- termeasures combining medical countermeasures and social filters can be set using parameters. Multiple policy scenar- ios can be assessed by simulating such countermeasures. The parameter setup method is explained in detail in the Parameter name Explanations Notes Example of Value Range of variance $EnSAF_home Environment attenuation filter （Family） Filter for space Be derived from environmental factor (for example humidity) 0: extinction 1: no attenuation 0.8 0-1 $EnSAF_office Environment attenuation filter （Workplace） 0.8 0-1 $EnSAF_school Environment attenuation filter （School） 0.8 0-1 $EnSAF_traffic Environment attenuation filter （Traffic） 0.8 0-1 $StSAF_home Disinfect attenuation filter （Family） Filter for space Virus is attenuated by disinfection with time. 0: extinction 1: no attenuation 1 0-1 $StSAF_office Disinfect attenuation filter （Workplace） 1 0-1 $StSAF_school Disinfect attenuation filter （School） 1 0-1 $StSAF_traffic Disinfect attenuation filter （Traffic） 1 0-1 $VSS_home Space density filter （Family） Filter for space to people The size of space can be infection de- fense. The larger size of space, the smaller risk of infection 400 Counting number $VSS_office Space density filter （Workplace） 1000 Counting number $VSS_school Space density filter （School） 800 Counting number $VSS_traffic Space density filter （Traffic） 400 Counting number Table 2 List of Externally Definable Parameters Page 4 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 $a Infection Parameter Fitting Parameter2 0.05 fixed $ability Vaccination ability Showing vaccination ability of health center. The value shows, how many people can be given a vaccine a day 300 Counting number $agent_ACPF Pollution defense filter Filter for space to people Protecting from pollution from space is possible with mask etc. 0: complete protecting 1: no protecting 1 0-1 $agent_EPF Discharge prevention filter Filter for people to space Wearing mask etc. can prevent pollution to space 0: no pollution 1: complete pollution． 0.3 0‐1 $agent_EnAAF Environment attenuation filter （People） Filter for people Derived from environmental factor (for example humidity) 0: extinct 1: no attenuation 0.8 0‐1 $agent_PC_baby antibody value（Infant） Showing, how much antibody value each generations have. This value can be varied from vaccine etc. 0: perfect antibody 1: no antibody 0.8 0-1 $agent_PC_middle antibody value （Middle-age） 0.3 0-1 $agent_PC_old antibody value （Elderly people） 0.3 0-1 $agent_PC_schoolc hild antibody value（Child） 0.8 0-1 $agent_PC_student antibody value（Student） 0.8 0-1 $agent_PC_young antibody value （Young people） 0.8 0-1 $agent_StAAF Disinfect attenuation filter （People） Filter for people. 0: Virus is killed off by etc sterilization. 1: no sterilization 1 0-1 $expname Scenario name Note: Letter string set here will be an output as a log-file. scenario1 Letter string $iso2 Hospitalization rate at level 2 The chance of being hospitalized at pa- thology level 2 0/100 0-1 $iso3 Hospitalization rate at level 3 The chance of being hospitalized at pa- thology level 2 80/100 0-1 $maxvactine Stock of vaccination The amount of vaccination stock by the government; they are available in the beginning of simulation and the number does not increase. 3000 counting number 2 Fitting parameter is a parameter, which determines the risk of infection, but that’s applied as fixed value here. javascript:goWordLink(%22sterilization%22) Page 5 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 $middle_jobrate Percent of middle-aged indi- viduals at work The percentage of the middle-aged at work/office; not an employment rate. The percentage tends to be lower for farming society. 0.8 0-1 $officeclose Workplace closure Whether or not to close office. If closed, the spread of infection stops but so is economic production. no yes, no $p23 Transition probability from level 2 to level 3 The chance of moving from pathology level 2 to 3. 0 0-1 $schoolclose School closure Indicator, whether closing the school or not at the beginning of vaccination. Infec- tion at school can prevent by closing school. no yes, no $st_attrate Percent of students at school This percent of students goes to school. 1 0-1 $strategy Vaccine policy What kind of vaccine policy should be made? For mass-random or only young generation? How much range should be the target at the network of infected peo- ple? young_red no, all, all_red, all_yellow, young, young_red, young_yello w $symptom Rate of apparent infection chance of the infection being an apparent one With the rate of 1-$symptom, pathology level will move to 2m which indicates unapparent infection. 90/100 0-1 $timer1 Duration of time for level 1 Duration of time for pathology level 1. After this, moving into pathology level 2 with the rate of $symptom. timer=+14/0:0 $timer2 Duration of time for level 2 Duration of time for pathology level 2. After this, moving into pathology level 3 with the rate of $p23 timer=+3/0:0 $vacdelay Delayed vaccination Duration of time from the start of the pathology level 3 until the start of vacci- nation; The longer the delay, the larger the number of infected at the time of vaccina- tion. 7 counting number $young_jobrate Rate of the youth at work The percentage of the youth at work / school who show up at the office / classes; not an employment rate．It tends to be lower in a farming society. 0.8 0-1 Page 6 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 next section. PARAMETERS AND SCENARIO LIST OF PARAMETERS To enable assessment of multiple policy scenarios, pathological scenarios, and city scenarios by simulation, this model is equipped with parameters that can be set ex- ternally. Here, in Table 2, we list a total of 40 parameters that can be set externally, along with some explanations. VACCINATION POLICY There are seven choices of vaccination policy, and the following is their explanations.  no vaccination  all random vaccination for all generations  all_red vaccination for 1）infected individuals， 2）their families，3）their colleagues at work or school（refer to Figure 13）  all_yellow vaccination for 1）infected individu- als，2）their families，3）their colleagues at work or school 4）work/school colleagues of the infected individuals’ family，5）families of the colleagues who goes to school/work with the in- fected individuals （refer to Figure 14）  young random vaccination for the youth  young_red vaccination for only the youth who are also one of the following: 1）infected individu- als，2）their families，3）their colleagues at work or school.  young_yellow vaccination for only the youth who are also one of the following: 1）infected indi- viduals，2）their families，3）their colleagues at work or school 4）work/school colleagues of the infected individuals’ family，5）families of the colleagues who goes to school/work with the infected individuals. TYPES OF SCENARIOS Here, we broadly divide the above parameters into three types: 1) city scenario; 2) pathological scenario; and 3) policy scenario. Furthermore, we classify the policy sce- nario into six categories; 1) filter policy for persons; 2) filter policy for person to place; 3) filter policy for place; 4) filter policy for place to person; 5) vaccination policy; and 6) other policies. The results of classification are shown in Figure 15. TABLETOP EXERCISES Here, we explain concrete methods and rules for play- ing tabletop exercises (gaming) using smallpox as a case study. Note that it is not suggested for the gaming players to read the next part “Simulation case studies” before play- ing gaming, as debriefing material for the gaming is in- cluded in the following section. SIMULATED TABLETOP EXERCISES This tabletop gaming exercise on infectious disease (Tabletop Gaming Exercise for Pandemic Protection of Smallpox) is to conduct exercises on and discuss emerging and re-emerging infectious disease (smallpox bio-terrorism countermeasures) at the experiential level using gaming simulation. This participation-oriented simulation tech- nique, called gaming (simulation), is extremely useful as a means for stakeholders to share their awareness and under- standing of problem situations, and to engage in communi- cation about risk. In the gaming simulation, participants become players and pursue the roles set in the game. In this way, they can assess the validity of scenarios and evaluate the risks and values of scenarios from the viewpoints of different stake- holders. In addition, gaming simulation can be used as a tool to share values regarding the significance and meaning of policies and decision-making, and as a study tool. Here, we created a model based on the process of smallpox contraction in a virtual city, using an agent-based simulation, and used it in the game. In particular, this agent -based smallpox simulation model enables us to evaluate the effectiveness of multiple countermeasure scenarios. Thus, the participants of this tabletop exercise formulate countermeasures against smallpox as policy decision- makers, execute simulations in accordance with plans, and evaluate the results. The gaming simulation is played by game participants (players) and a facilitator who facilitates the game. Here, the players form several teams, and each team plays the role of policy decision-maker and determines the smallpox countermeasure policy scenarios as described below. Com- puter simulations are then conducted on the countermea- sure scenarios developed by different teams, and the results are compared and discussed. This is the basic flow of the tabletop exercise gaming. The facilitator controls the overall flow of the game, assists in the execution of the simulation, holds a debriefing about the results, and coordinates discussions between the players on crisis management of smallpox countermea- sures. OUTLINE OF SMALLPOX BIOTERO TABLETOP EXERCISES Specifically, a smallpox countermeasure tabletop exer- cise game proceeds as follows. The duration of the gaming is between 90 and 180 minutes, including debriefing. Here, we explain the process using the minimum configuration (90 minutes). The minimum configuration can be used with the assumption that the players have a certain level of knowledge about smallpox. Each team determines its coun- Page 7 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 termeasure scenario using the risk management policy de- scribed below. Then we simulate the scenario by “Anti-Smallpox Simulated Tabletop Policy Exercise Program”, that is de- veloped on SOARS < http://www.soars.jp> as executable program on Java 1.5 or later. The simulation supported TTE is developed by Hiroshi Deguchi. It can be downloaded from the Center for Agent Based Social Sys- tems Sciences (CABSSS) . Next, the agent-based simulation of the scenario of each team is executed on the team’s PC. Note that progress of the simulation is shown graphically, so it can be dis- played for all to see using a projector, if desired. Simula- tion time depends on the processing power of the PC and also on the scenario. As a guide, a process of about 200 days can be simulated in approximately 15 minutes. After the simulation, a review discussion (debriefing), led by the facilitator, is held on the results of each team and the rea- sons for the particular results that were produced. 90 minutes case of the Simulated Tabletop Exercise  00～20 Explanation of Smallpox and TTE  20～50 Team Creation & Scenario Selection  50～65 Simulation  65～90 Results Analysis and Debriefing PLAY OF TABLETOP EXERCISE 1) Overall explanation by the facilitator The facilitator will give an overall picture of smallpox, the structure of the virtual city on which the simulation will be conducted, possible coun- termeasures, as well as how to proceed with the game. 2) Team formation The minimum number of participants must be at least four, divided into four teams. Thus, although there is no upper limit, it is desirable to limit the number of participants to 20 persons for the smooth functioning of teams. 3) Scenario Selection In this gaming, vaccination-related scenarios are mainly selected as the basic strategy against small- pox. Here, we will select scenarios by combining the following six factors. 1. Vaccine stocks, as a proportion of population (30%, 60%, 100%): 3 types 2. School shutdown (yes/no): 2 types 3. Number of vaccinations per day per 10,000 people (20/day, 300/day): 2 types 4. Delay in commencement of vaccination (none, 7 days, 14 days): 3 types 5. Targeted generations for vaccination strategy (all, young): 2 types Card Explanation Choice(the number of sheets） Vaccine stock card Stock rate of vaccine to population 30％（2 sheets） 60％（1sheet） 100％（1sheet） School closure card Indicator, closing school or not at the beginning of vaccination ○（2 sheets） ×（2 sheets） Amount of vaccination card Amount of vaccination per 10,000 people 20people/day（1sheet） 300people/day （3 sheets） Delayed vaccination card Period of time, from the begin- ning of simulation to the begin- ning of vaccination 0 days（1 sheet） 7days（2 sheets） 14days（1 sheet） Generation target strategy card Indicator, which generation will be targeted on vaccination all（2 sheets） young（2 sheets） Social target strategy card Indicator, how to make vaccina- tion to the people involved of in- fected people random（2 sheets） red（1 sheet） yellow（1sheet） Table 3 Scenario Card Selection Page 8 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 6. Targeted social networks for vaccination strategy (random, red, yellow): 3 types These factors are not selected freely. The following selection methods are used. a) Preparation of cards Prepare some cards for each of the six scenario factors for selection. (As an alternative, if you cannot prepare cards, you can write the factors on a whiteboard.) b) Determination of selection order Team representatives determine the order of their teams from No. 1 to No. 4 by playing rock-paper- scissors or rolling dice. c) Card selection Select scenario cards using the “Weber Method,” as is shown in Figure 17. For the first card selection, the No. 1 team, as deter- mined in b), selects its preferred scenario factor out of the six factors, and takes the selected card from the factors. In the same way, the No. 2, No. 3 and No. 4 teams, in this order, take one card from the remaining cards of scenario factors. For the second card selection, the teams select one card in the same way, but this time in reverse order (team No. 4, then No. 3, No. 2 and No. 1). Note that the scenario factors they chose in the first card selection cannot be cho- sen again. In the same way, card selection is conducted six times so that each team determines all scenario factors. Ensure that the teams understand this determination method thoroughly in advance, and ask them to discuss the selection order of the scenario factors. d) Scenario Selection After selecting the policy scenario then find the scenario number from SmallpoxPandemicProtectionScenario216.pdf as follows in Figure 18. Parameter names corresponding to each option are as follows. 4) Starting Simulation Program As a software environment we need Java 1.5 or more on any operating system. Anti-pandemic simulation program is an executable application o n J a v a t h a t i s d e v e l o p e d o n SOARS. Please unzip(extract) downloaded simulation.zip file. Then simulation folder will appear. You can start the program by clicking run.jar under Java 1.5 environment. Start the simulation program and select the scenario number and click run button as Figure 19. The progress of the simulation will be displayed graphically as Figure 20. This enables the facilita- tor to make live comments on the progress of in- fection as the graphs are projected. The progress of infection of the four teams is visible, if all four simulations are displayed at the same time. This could be achieved using a split-screen setup or with four projectors. DEBRIEFING The simulations end after about 15 minutes. When they are finished, the debriefing starts. In the debriefing process, the players compare scenarios and the results of the scenarios. They then discuss what is important for smallpox countermeasures. The following points are impor- tant when the facilitator is coordinating the debriefing dis- cussion. 1) About the gaming structure Here, the teams cannot select scenarios freely. This is because players tend to always select the ideal situation when they are not restricted, mak- ing the exercise less than useful. It cannot be shown that scenarios are limited by actual organ- izational structure or resource restrictions. Instead, the selection of scenario cards by teams, according to the importance they place on different scenario Card Parameter Value Vaccine stock card $maxvactine 3000, 6000, 10000 School closure card $schoolclose Yes, no Amount of vaccination card $ability 20, 300 Delayed vaccination card $vacdelay 0, 7, 14 Generation target strategy card $strategy all, all_red, all_yellow, young, young_red, young_yellow Social target strategy card $strategy Table 4 Correspondence of Card Parameters Page 9 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 factors, reflects differences in ideas between the teams. 2) Total number of strategies There is a total of 216 scenario combinations (3 × 2 × 2 × 3 × 2 × 3), i.e., stock cards: 3 types; school shutdown cards: 2 types; number of vaccinations cards: 2 types; vaccination delay cards: 3 types; targeted generations cards: 2 types; and targeted social networks cards: 3 types. Though omitted here, there are actually many more types of coun- termeasures to be considered, including “filter” type countermeasures, such as shutting down of- fices, spatial density countermeasure, and virus discharge restriction countermeasure. The possi- bilities of such countermeasures can be discussed. 3) Countermeasure implementation issues In order to make it possible to execute counter- measures, it is essential to have the programs and project management to execute them. Therefore, discuss the social and economic circumstances surrounding infectious disease countermeasures, e.g., How these countermeasures can be imple- mented? What is the economic impact of imple- menting these countermeasures? SIMULATION SUPPORTED TTE CASE STUDY Tabletop exercise against bio-terrorism by smallpox is already constructed, that is called "Dark Winter". This is one of the famous tabletop exercises (TTE) for policy making against bio-terrorism risk. On the other hand the TTE sup- port only a single scenario and there is no explicit infection process model inside the TTE. Simulation supported TTE or Hybrid simulation are hybridization of gaming simula- tion by human players and agent based simulation by ma- chine agents, that will support better human communica- tion and mutual learning among agents that include the decision makers who use the model. Figure 21 shows the concept of hybrid gaming simulation and simulation sup- ported tabletop exercise. The TTE was executed at Global Security Research Institute, Keio University (G-SEC) on February 23rd in 2008. The following picture (Figure 22) is a scene of TTE by political stakeholders against bio-terrorism. The player teams consist of professionals for anti bio-terrorism. Figure 23 shows the results of the four teams played at Global Security Center. The TTE supports 216 policy scenarios in the model. The total landscape of 216 scenarios is shown in figure 24, that is phase diagram of small pox infection pattern by simulation for 216 policy scenarios. X axis means the num- ber of vaccinated persons and Y axis means the number of infected persons. The landscape shows that the total num- ber of vaccinated persons becomes an important factor for controlling the number of infected persons even if other policies have failed. REFERENCES Deguchi, H. (2004). Economics as an agent-based com- plex system: toward agent-based social systems sci- ences, Springer. Deguchi, H., Kanatani , Y., Kaneda, T., Koyama, Y., Ichi- kawa, M., & Tanuma, H. (2006). Anti Pandemic Simu- lation by SOARS, SICE-ICASE International Joint Conference, Oct. 18-21, Bexco, Busan, Korea. Putro, U. S., Novani, S., Siallagan, M., Deguchi, H., Kanatani, Y., Kaneda,T., Koyama, Y., Ichikawa, M., & Tanuma, H. (2008). Searching for Effective Policies to Prevent Bird Flu Pandemic in Bandung City Using Agent-Based Simulation, Systems Research and Be- havioral Science, 25, pp.1-11. http://www.gsec.keio.ac.jp/english.php http://www.gsec.keio.ac.jp/english.php Page 10 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 1 Outline of simulation epidemiology Figure 2 Pathological transition general model Page 11 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 3 Pathological transition model for smallpox Figure 4 Pathological transition model for influenza Page 12 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 5 City Structure Model Figure 6 Family Structure Model Page 13 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 7 Human Activity Model Figure 8 Person to Place Contamination Figure 9 Place to Person Contamination Page 14 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 10 Filter Model Figure 11 Four Types of Filters Page 15 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 12 Vaccination as a Filter Figure 13 Red Vaccination Policy Page 16 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 14 Yellow Vaccination Policy Figure 15 Three Layers of Scenarios Page 17 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 16 Policy Scenarios Selection Figure 17 Weber Method for Card Selection Page 18 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 18 Anti Pandemic 216 Policy Scenarios Figure 19 Start the Simulation after Selecting a Scenario Page 19 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Vaccine recipient Uninfected people Hospitalization people Infected people fatalities Figure 20 Real Time Graph of Simulation Process Figure 21 Concept of Hybrid Simulation & Simulated Tabletop Exercise Page 20 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 22 Simulation Supported TTE executed at Global Security Center Figure 23 Results by Four Teams Page 21 - Developments in Business Simulation and Experiential Learning, volume 38, 2011 Figure 24 Total Landscape of 216 Scenarios Table of Contents Volume 38, 2011 Simulated Tabletop Exercise for Risk Management - Anti Bio-terrorism Scenario Simulated Tabletop Exercise From Business Games to Simulations - Simuworlds & Microworlds Demand Equation Redux: The Design and Functionality of the Gold/Pray Model in Computerized Business Simulations Managing Client-Based Learning: Insights from Successful Teaching Project Courses in Marketing Tracking Forecast Error Type, Frequency and Magnitude with the Forecast Error Package Responding to Facilitate Collaboration Simulating Sudden Change and the Value of Timely Information Managing Human Resources Simulation A Study on Collectivism and Group Decision-Making: An International Comparison of Japan, China, and Russia Using a Gaming Simulation The Use of Management Games in the Management Research Agenda Gaming On-Line: A Simulation Application Positioning and Performance in Simulated Networks Supply Chain Management: A Simulation Application Simulation as a Teaching Method in Strategic Management Distance Studies Entrepreneurship: A Game of Risk and Reward Phase II: The Start-Up Return to the Paradise Islands: From Confrontation to Cooperation Effect on Market Performance of Displaying Supply and Demand Curves in a Business Simulation Appreciating Complexity: The Chief of Staff of the Army Game Managing Organizations: Experiential MBA Course Teaches Alternatives to the Machine Model A Simulation Model for Analyzing the Night-Time Emergency Health Care System in Japan The Continuing Evoluation of Assessing Project Management as an Academic Learning Outcome (ALO) Should College Instructors Change Their Teaching Styles to Meet the Millenial Student? The Mouse Game and its Effects on Team Interdependence Learning from the Gulf Oil Spill to Prepare for a Brighter Future: A New Game Engaging Stake Holders in Triple Bottom Line Accounting & Strategic Planning Video Killed the Biblio Star: The Impact of Digital Media on Student Learning Outcomes Exploring Motivation: Using Emoticons to Map Student Motivation in a Business Game Exercise An Alternative to PC and Internet Based Simulations: The Internet Integrated Mode MiddleState University -- A Crisis in Education Complexity Avoidance, Narcissism and Experiential Learning Examining the Cognitive, Affective, and Psychomotor Dimensions in Management Skill Development Through Experiential Learning: Developing a Framework A Situational Leadership Exercise Based on the Biology of a Starfish JOGAI CEFET -- The Industrial Administration Undergraduate Game Would You Take a Marketing Man to a Quick Service Restaurant? Modeling Corporate Social Responsibility In A Food Service Menu-Management Simulation Use of a Simulation in a Large Class Environment for a Marketing Principles Class: A Qualitative Analysis of Whether Learning Objectives were Met A Team Based Information Literacy Exercise ABSEL Marketing Communications Plan An Interdisciplinary Study of the Impact of Playing a Marketing Simulation Game on Student Knowledge of Management Accounting/Finance Principles Analyzing Construction Planning of Interiro Finish Work of Apartment Building by Simulation Doing Murder One Again The Simple Business Game and Simulation Transfering the Knowledge of Middle Management to Novices Infectious Disease Simulation Model for Estimation of Spreading Understanding the Relative Influence of Several Factors in ERP Simulation Performance: An Exploration of Ecological Validity Tragedy of the Commons: An Exercise Using Clickers to Illustrate and Teach a Key Concept in Negotiations The Meaning of Firm Demand in Business Simulations If the Games Work, Why Aren't More Faculty Willing to Play? 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