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‡Œ&•ì&®º¦ @' ‡ì•L•ü&®º¦€?‡Œ ‡Œ•ì"•¬¸&¸•¼¸$¸•œ'•Ü&‡l'‡ì‡Œ•Ü&®º¸¦?• ‡ì•œ¸&¸G•¬±¦& ‡Œ¸¦•Ì&®º¸¦& ‡ŒG•,&®º¦‡Œ ‡ì•Ì•¼&®º¦@‡Œ ‡Œ•,F•L²•\°&®º• •\/•¼¸)¸•\,•¼#¦€?±¸¦€?±¦ ‡Œ••\?®º•Ì ±¸•ü &•\ ²•,°&®º¦€?±¦€?±¦€??‡ì'‡œ•ì&®º¦€?&®‡Œ'‡ì•,?®º•¼$•ܕ젹&¸¦€=‡ŒJ¾>¾¹&¸¦–CÜ*¾•œ¾¹&¸¦‡ì ¾•Œ•¬¹&¸¦–C‡ì,•| ²»SenParDim°&®º• •l &¦€?±»Parameter Level Selector%•Ü ²»SenParDim°$•ì ²»SenParDim®°#•ì ²»SenParDim°±•ü ²»SenParDim°?ˆ$•Ü•œ¹&¸¦€= ‡ì•Œ¹&¸¦–C ‡ì•|¹&¸¦ ‡ì•¬¹&¸¦–C <‰¸šš±šš&š'š œ&š&Gš$š±š%šœ%œ‰¸šš±š&š&Gš$š'š±š œþ²»SenParDim°&®º• »sensitivity is on&¦€?±•\ %•l ®º²»SenParDim°$•| ²»SenParDim°#•| ²»SenParDim°±•< ²»SenParDim®º°pþH¡7L¿‚‚ôK&Internally defined simulation time.ºThis model stops exploration if it goes out of feasible region and uses absolute deviation (rather than standard deviation) in determining the exploration noise standard deviation.NWhere between maximum and minimum action standard deviation we want to standZWhat is the length of delay between action and payoff as understood by the organizationrŠWhat is the value of action as used by organization in specific decisions. It is determined by the "Activity Values" at each time and the exploration pink noise term and are used to determine the decided "Fraction of Resource" to be allocated to each decision. For the regression model, having 0 action would create problems in regression, so a minimum of 1e-5 is given to the Activity Value.ufNormalizing Activity Values is helpful for having same exploration parameter for different models.s.¦When the external noise starts (after which time the Activity Value needs to be switched to the constant value it had at the time of start of the external noise))BGives 0 for times before external noise start time, otherwise 1e ZAn exogenous exploration input to see how the system reacts to learning opportunitiessvTraces the Activity Value with one time step. Used for evaluating the effect of one single shock on Activity Value.uežWhat is the distribution of resources in action associated to perceived payoff, used in myopic search model to create the target desired allocation valueses‚In order to move towards the best paying action suggested by different algorithms, what should be the desired activity value be.bfPut 0 to use the pink noise for exploration, use 1 to use the exogenous noise to stimulate the modeldžThis is the averaging time used in comparing the perceived payoff with historical in order to determine whether a positive change has been made recently..ZThe percentage distance of perceived payoff from historical payoff if perceived payoff is higher than history. Higher payoff than history as a result of exploration suggests that there is still significant improvement possible by more exploration and this increases the tendency for exploration by increasing the standard deviation of searchhªThe Activity Values indicated by the last action. It is used as desired value for future, if the payoff generated by last action is better than the historical payofffThis flag determines wheather there has been any parameter estimation in the feasible range or not. 0 indicates lack of any feasible estimate for peak, which triggers use of random desired Activity Value rather than what calculated based on the estimationio¶This variable determines if change in payoff is perceived or not. If it is, then the myopic algorithm will move the Activity Value towards/away from the indicated Activity Value.e."Rate of adjustment of reward system toward desired value. In case of regression model, the initial values are not adjusted until enough exploration is done and the first real estimation of variables is done. Moreover, if there is no feasible estimate, regression stops updating values.lu®How attractive is the action in the mind of the organization. Note that Activity Values are not meaninful for the Rnd Walk model as it does not use them in its procedure.edfWhite noise input to the pink noise process. A normal white noise will create a normal pink noise.noÎThis generates the stream of normal random numbers used to generate the Pink Noise for the exploration. The Noise stream used is same for all the models and only differes accross different alternatives.ti>How much the ratio of payoff to historical should be bigger than one, so that the organization decides to move the desired Activity Value to what indicated by the discovered action profile. It can be a measure of sensitivity of decision-maker to exploration results, as well as the intertia in changing the goals.oavThis switch tells when the initial exploration period, during which there is no parameter estimation, is finished.is*This gives the inverted matrix for XT*XorThis matrix is the version of XTX used for inversion. At the beginning, when there is no prior data for estimation, this is simply the identity (an arbitrary choice) matrix. After gethering enough information for making estimations, this matrix takes its real value from the data.eÚThe summations of history of XT*X matrix. The initial value is given to be 0, as it is the case when there is no data available. Forgetting process enables adjustment of model in longterm if the environment changesharThe allocation fractions for the last sample taken. It instantly adjusts the Current Sampled Payoff to Achieved to Optimal Payoff, on the sampling times for calculating the distance traveled. This formulation could be unpacked (you can see the unpacked example in case of Desired value for Myopic equation ), in the interest of space, it is kept in the summarized way.e~The payoff at the last sampling time, before the current sample. It instantly adjusts the Last Sampled Allocation to Current Sampled Payoff, on the sampling times for calculating the distance traveled. This formulation could be unpacked (you can see the unpacked example in case of Desired value for Myopic equation ), in the interest of space, it is kept in the summarized way.d úv exponent which determines the speed of change in the correlation slope epªv on the senstivity parameter level 1[p1]. With a value of 4, it means that correlation slope will change between 4^-1(=0.25) and 4^1(=4) times of its base level.ev2The correlation time constant for Pink Noise.oi’Initial value for prior mental model of different activity values. If not using random initial allocation, gives 0 for value of all activities.vifOne of the random numbers generated to create the random initial condition for the Activity ValueVafOne of the random numbers generated to create the random initial condition for the Activity ValueVaBDetermines how far in the action space we are willing to exploreerThe summations of history of XT*Y matrix. It starts at 0 because at the beginning there is no history for data.r ²Before the estimation of parameter starts, to ensure feasibility of inverted XTX, we use an arbitrary matrix (identity here) to be inverted, to avoid matrix inversion errorsrrVFraction of simulation time that the movement has been upward on the payoff surfacesu^Fraction of simulation time that the movement has not been up or down on the payoff surfacesuZFraction of simulation time that the movement has been downward on the payoff surfacerf.The distribution of resources at any time tºThe rate of payoff generation from past actions as perceived by decision maker at the current time, if there is a delay. Different orders for smoothing of information can be used here.dRThe rate of payoff generation as perceived by organization at the current time.enzAction associated to perceived payoff, after taking into account different combinations of delays in the former steps.me6Instantanuous payoff to (longterm) optimal ratio.al–This variable is just added to make sure that there is no unaccounted delay (1 time step delay) when the "Perceived Time To Influence Payoff" is 0yo^The value of past actions determining current payoff, after selection of desired delay type dþThe action (allocation) which is considered to be generating the current perceived payoff so that the causality is attributed to that. If there is no perception delay, then the "Aux Action associated to real current payoff" is associated with payoffthrIn one period, how many percent should we increase/decrease to call it increase/decrease rather than no change n¢Gives 1 if change of payoff between two sampling period has been faster than threshold, -1 if it has been less than negative of threshold and gives otherwise.otvThe integration of time that the movement on action space has been with no significant change in altitude (payoff)e ŠThe allocation fractions for the last Sampling Time (Distance Sampling Time). It instantly adjusts the Current Sampled Allocation to Fraction of Resource, on the sampling times for calculating the distance traveled. This formulation could be unpacked (you can see the unpacked example in case of Desired value for Myopic equation ), in the interest of space, it is kept in the summarized way.anDetermines (and stops at till next sampling time) the distance between two samples locations on action spacecNThe integration of time that the movement on action space has been upwardenvDuring sampling time, how much should we increase/decrease the payoff to call it climb/descend rather tahn no changen¢The last sampled time, used to calculate the distance beween current and last sampled positions. It instantly adjusts the Last Sampled Allocation to Current Sampled Allocation, on the sampling times for calculating the distance traveled. This formulation could be unpacked (you can see the unpacked example in case of Desired value for Myopic equation ), in the interest of space, it is kept in the summarized way.riNThe integration of time that the movement on action space has been downwarden.Gives 1 if it is sampling time, otherwise 0ot¦The periods of looking at incoming data and comparing them to the sample from last sampling time (the time between two sampling for computing the distance traveled)eFHas the algorithm converged (0 for not converged, 1 for converged)corThe payoff filtered from high frequency noise, used as the main measure of perceived payoff by the organization.i6Amplitude of sine wave in input (fraction of mean).nc&Period of sine wave in input. nce cvAfter converging, how much lower than historical should we get, in order to consider switching back to non-convergednrThe value of normalized standard deviation going under which we assume the system has converged to some answeromRThe value of variance going above which we switch back to non-converged postionge~How much lower than the average historical vairance to we want to go in order to consider the algorithm to have convergedcoÊIn correlation model, the slope of logistic curve at the origin is determined by this parameter. A high value of slope suggests aggressive reaction to perceived relationship between action and payoff.aâPut 0 for starting at an allocation of same thing for every action. Put 1 for starting at random allocation start (used mostly for sensitivity analysis). Go to Myopic Search view to adjust the parameters for random start.om²Â mum parameter level for sensitivity. For Correlation Slope at origin, 4 is risen to the power of the given numbers here and the resulting number is multiplied by base casey zThe period for runing a new regression and using the new estimates for finding the peak location on the payoff landscapef"" ation of action associated to perceived payoff, used to track performance of systeme Æ" 1 when sensitivity analysis over "sensitivity parameter level" vairables is on, put sth else when not. wVÈvalue given here inputs each of the model paramters under sensitivity analysisy ZShowing how strong the differences in payoff will be reflected in action value updates.ueF{ base value of sensitivity parameters under parameter selector 1 sâThe time horizon over which the organization looks to attribute causality in the correlation model. This time horizon is used to calculate the average and variance of action and payoff in order to calculate the correlation c6Time to forget the reinforcements by organization.an®Èvalue given here inputs each of the model paramters under sensitivity analysisy 2Selects the values for changing some parameters in the sensitivity range of them. These parameters include "Correlation Slope At Origin", "Evaluation Period", and "Reinforcement Power". Its value changes between 0 and 1, where 0 gives the minimum level and 1 gives the maximum level for the parameter.ar~Summation of actions. Used to make sure the consistency of model, because it should be always equal to the total resource. r¢Èmaximum value of parameters for searchforSum of effective actions at each time. It is used for tracking why payoff may go beyond (equilibrium) optimum) RThe ratio of actions remembered to be the case in the beginning of simulationimŠThe delay between action and payoff and represents how long it takes for each alternative to become effective and influence the payoffth¦This is the time constant of forgetting second order data. A large value ignores forgetting, which is fine in our case since payoff landscape is fixed through time.r.Rate of forgetting the data for XT*X matrixXT–The forgetting of XTY matrix data. If forgetting is active, the model can adjust to changes in parameters of payoff function without any problem. p Start time for the ramp input.>Determines if more than 1 parameter has the highest valueimÞThe Matrix Y representing the (Log) values of dependent variable (Perceived Payoff) is generated from the values of this variable. Log is used for Cobb-Douglas payoff while for linear payoff the normal values are used.rDetermines which of the estimated parameters is bigger than others and assigns 1 to that, otherwise assigns 0 pCalculates the peak place on the payoff landscape. For the cobb-douglass function, if the estimate for some parameters is negative, it ignores them by using 0 rather than that estimate. If all estimates are negative or 0, then a zero value for all desired actions is suggested. d†The matrix X representing the values of independent variables, Action Associated with Perceived Payoff, is captured by this variable ^The estimated parameters on last time step, used for keeping track of historical estimates.f žRuns the regression and calculates the parameter estimates for payoff function on each evaluation period. Keeps these values untill next evaluation period.lu>Gives 1 if it is evaluation/regression time, 0 otherwise. i>Generates X transpose*X matrix and accumulates it over time0BGenerates the X transpose* Y matrix and accumulates it over timebšThe accumulation of payoff variance over time, used to determine when the payoff variance gets too low and we can assume convergence to have happened.orŠThis pink noise term introduces the exploration of new action profiles into the presumably best action coming from the Activity Value.l ÞThe percentage of achieved payoff to the optimal payoff acheivable through an equilibrium policy. The only difference with "Achieved to optimal payoff" is the high frequency filter to remove the noise on this variable. enHow much of resources is given to each action at the any time. This value will determine the (future) payoffcVIn accounting for delays, the organization should decouple two delays and account for them in two steps: first is the delay between action and real payoff and then the delay between real payoff and perception of real payoff. This variable keeps track of first delay and accounts for that by associating actions to the real current payoff.s"Start time for the random input.uZDifference of current action from its average, used to compute the variance in actionto~Effective actions normalized by their efficiency. It is used for unit consistency and easier manipulation of payoff functioni6The standard deviation of the pink noise process.ia>The reinforcement given based on received payoff at any timeaêThe value of past actions determining current payoff. This variable is used only if past actions are determining payoff (there is a delay between action and payoff). If the deley is 0, then action itself will determine the payoff inExponential average of action over "Action Lookup Time Horizon" used to compute Action-Payoff CorrelationconExponential average of payoff over "Action Lookup Time Horizon" used to compute Action-Payoff CorrelationcoNAverage payoff variance devides the accumulated variance to time in order to get an overal average of payoff variance through simulation. However, after it converges, to avoid depreciation of accumulated payoff and arbiterary decline of cut off variance with time, the accumulation of payoff is only devided by old convergence time.lJThe average value of allocation for different resources in recent monthsi&Accumulates the vairance in payoffAcrConvergence time of algorithm, shows when we consider the algorithm to have converged to some allocation policycoZDifference of current payoff from its average, used to compute the variance in payoffto~Change in the pink noise value; Pink noise is a first order exponential smoothing delay of the white noise input.ofbThe percentile change in payoff as perceived by organization, attributed to change in actionserNThe Eucledean distance of current allocation policy from optimal allocationti6Height of step input, as fraction of initial value. oþial value.erÂThe quantity to be injected to noise, as a fraction of the base value of Input. For example, to pulse in a quantity equal to 50% of the current value of input, set to .50. alEnd time for the ramp input.0>Slope of the ramp input, as a fraction of the base value.r ÚcNPayoff smoothed to remove noise and give an average of it over recent past a>Payoff Variance calculated to determine the convergence timed:payoff in the old time used to look at trend of payoffme.Determines the lowest level of explorationes‚How fast payoff is changing in a positive direction and therefore how close to optimum the organization perceives herself to beecTime for the step input.lBThe historical payoff used to guide exploration strengthan).n ’Can introduce Exogenous Noise in Payoff, showing how payoff might be determined not by factors under control of or anticipated by organizationtr6Noramlized standard deviation of payoff percentageta"The (X-Ave(X))^2 for payoff=Xge.White noise input to the pink noise process. Nod of sine wave in customer demand. Set initially to 50 weeks (1 year).d*The payoff generated at the current timeh*Time at which the pulse in Input occurs.i¾ðcts the values for changing some parameters in the sensitivity range of them. These parameters include "Time To Change Activity Value" and "Time to Forget Reinforcement". Its value changes between 0 and 1, where 0 gives the minimum level and 1 gives the maximum level for the parameter.s 2The maximum value of parameters for sensitivitymu2The minimum value of parameters for sensitivitymu.ðbase value of all sensitivity parametersa>How fast the Activity Value system moves towards the desired ªThe averageing time for payoff in order to calculate variance. It needs to be high enough to filter the very fast exploration and enable the calculation of variance.d Caluculates the equilibrium optimal of the payoff function, depending on the type of the payoff function. Note that for Cobb-Douglas functions with increasing return to scale, this is not the global optimum, in presence of delays between action and payoffncb¼value of optimal allocation fractions should be entered here at the beginning of simulationre2Covariance between payoff and different actionsce~The smoothing time frame to look at payoff and compare it to optimum. It determines what frequencies of noise are filteredwh^The delay time for perception of payoff, as understood and accounted for by the organizationd†Put 1 if you want to normalize the power of actions to sum into 1 (constant reutrn to scale). Put 0 if you don't want normalizationalrIf using Cobb-Douglas payoff function, this determines the power term for different actions in payoff functionm âThis is used only for Cobb-Douglass payoff function where it determines the power term for different actions in determining the payoff. If constant return to scale, the same ratios are preserve while sum of them is put at 1 a&Rate of forgetting the reinforcement NWhat kind of smoothing process is used for perception. Use any integer >=1s rThe value of switches for selecting which kind of delay we use in calculating the change for action and payofflcNWhat is the delay order of action-payoff, as perceived by the decision maker-¶For the delay between action and payoff, if order N delay is used, then what is the order of the delay. The other options are having no dely and having fixed (infinite order) delayevHow attribution of different allocations and payoff suggests new desired actions. This effect is centered around 1 with a markup to change the desired action depending on the direction of correlation perceived. A logistic function determines the shape of the effect so that it is bounded between 0 and 2 and its slope at origin (correlation=0) is a parameter of the model(cjDecides whether to use a linear or Cobb Douglas payoff function. Put 0 for linear and 1 for Cobb DouglasoÂsratio of desired activity value to activity value shows where the mental model stands in regard to where it is heading for. This variable is not used in the model but is only an output. u6What is the sum of Activity Values currently usedthVExponential average of Change in Action^2, used as proxy to the variance of action2,zCorrelation between action and payoff, calculated from exponentail average, variance and covariance of action and payoffaVExponential average of Change in Payoff^2, used as proxy to the variance of payoff2,BThe standard deviation in percentage of payoff considered to be natural. If standard deviation of payoff in postive direction is significantly higher than this value, exploration continues with full power, however, if payoff shows significantly less improvement, then minimum standard deviation is used for the noise.stvMain noise seed of the model, used to initiate payoff noise, action pink noise and also initial value in some cases n>How fast we change the noise standard deviation for actionge¢The total amount of resource to be distributed continuesly (at any time, the 100 units should be distributed in some way). It is assumed constant in this model.w~Tells us which type of delay do we want to choose for calculating old trend. select 1 for OrdN order and 2 for fixed delay.eªTime interval used to look at the change of payoff and action, used in historical trends. The history is used in Myopic algorithm to compare payoff with its past valueribHow many times of the real initial payoff is the old initial payoff (what in the mind of people)i~Change in the pink noise value; Pink noise is a first order exponential smoothing delay of the white noise input.g 2The correlation time constant for Pink Noise.co:Pink Noise is first-order autocorrelated noise. Pink noise provides a realistic noise input to models in which the next random shock depends in part on the previous shocks. The user can specify the correlation time. The mean is 0 and the standard deviation is specified by the user.vi6The standard deviation of the pink noise process.daBThe delay to perceive what is happening to payoff and actionse šTells us which type of delay do we want to choose for calculating payoff. select 1 for having no delay, 2 for OrdN order delay, and 3 for fixed delay.r ^Whether each delay type switch is one or off. Used to determine the delay type used between action and payoff which can be No delay, OrdN delay and fixed delay. It is also determining the type of delay used by the decision maker to account for the delays between action and payoff. As a result there is no mistake in type of delay being discounted. êHow efficient actions are in giving payoff. This parameter is used to give weights for actions in linear function when using that, it also makes the actions dimensionless, however, has no numerical impact on the cobb-douglas payoff.uvCoeficient for determining payoff from normalized actions. It is only used for scaling and giving unit to the payoffd" Simulation Control Parameterse &The time step for the simulation.rs&The final time for the simulation..&The initial time for the simulation...The frequency with which output is stored.Th~Updates the travel distance if it is the distance sampling time step and the model has not yet converged to some allocation.sThis metric keeps track of the distance the organization travels on the "fraction of action" space and therefore gives an idea of the energy consumed for exploration before convergence. It stops growing as soon as the organization converges to a policy.mifThe value of optimal allocation fractions should be entered here at the beginning of the simulationul4Control Parameters&The time step for the simulation.&The final time for the simulation.&The initial time for the simulation..The frequency with which output is stored.pation of actions. Used to make sure the consistency of model, because it should be always equal to the total resource.êHow efficient actions are in giving payoff. This parameter is used to give weights for actions in linear function when using that, it also makes the actions dimensionless, however, has no numerical impact on the cobb-douglas payoff. is used only for Cobb-Douglass payoff function where it determines the power term for different actions in determining the payoff. If constant return to scale, the same ratios are preserve while sum of them is put at 1ÂThe ratio of desired activity value to activity value shows where the mental model stands in regard to where it is heading for. This variable is not used in the model but is only an output.\€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€öÀ@@@€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€?€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€?€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö@@€ö€ö€ö€ö€ö€ö€öÍÌÌ=€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö–C€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö @€?€ö€ö€ö€ö€ö€?€?€?€?€?€?€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö@@€ö€ö€ö€ö€?€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€öÍÌÌ= ×#< ×#<€ö€ö€ö€ö€ö€ö?€?€?€?€?€?€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö @€ö€ö€ö€ö€ö€ö@€ö€ö€? @€? @€ö@@€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€?€ö€ö€ö€ö€ö@@@€?€?€?€?€?€?(knN(knN(knN(knN(knN€?€?€?€?€?€?€ö€ö A@A€?ÍÌL>HBHBHBHBHBA€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€?€?€?€?€?€ö€ö€ö€ö€öRI9€=©_ãWÈB€ö€ö€ö€ö€ö A A@@€ö@@ ×#<ÍÌÌ=€ö€ö€ö€ö€ö€?€?€?€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö@@€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö€ö¡7L¿ ô///---\\\ :GRAPH ACTION_PATH :TITLE Action path :X-AXIS Action[a1,Crr] :X-MIN 0 :X-MAX 100 :SCALE :VAR Action[a2,Crr] :Y-MIN 0 :Y-MAX 100 :GRAPH ACTION_VALUE_PATH :TITLE Action value path :X-AXIS Action Value Normalized[a1,pfr] :X-MIN 0 :X-MAX 1 :SCALE :VAR Action Value Normalized[a2,pfr] :Y-MAX 1 :GRAPH ACHIEVED_TO_OPTIMAL_PAYO :TITLE Achieved to Optimal Payoff Percentage :SCALE :VAR Achieved to Optimal Payoff Percentage[Rgr] :Y-MAX 120 :GRAPH ACTION_[A1] :TITLE Action [a1] :SCALE :VAR Action associated to perceived payoff[a1, Model] :SCALE :VAR Action Value[a1, Model] :SCALE :VAR Action[a1, Model] :GRAPH ACTION[A2] :TITLE Action[a2] :SCALE :VAR Action[a2, Model] :SCALE :VAR Action Value[a2, Model] :SCALE :VAR "Change in Payoff Perceived?" :GRAPH EXPLORATION_PATH :TITLE Exploration path :X-AXIS Pure Exploration[a1,Myo] :DOTS :SCALE :VAR Pure Exploration[a2,Myo] :GRAPH PAYOFF_AND_OPERATION_ACT :TITLE Payoff and Operation Action Value :SCALE :VAR Payoff[Rgr] :SCALE :VAR Operational Action Value[a1,Rgr] :L<%^E!@ 1:C:\Documents and Settings\hazhir\Desktop\Educational\Learning Model\Spring 2003 Research\Final Simulations\Jun14-BaseGraph.vdf 9:Jun07-SensAll-3 18:X:\Spring 2003 Research\SensAll.vsc 20:X:\Spring 2003 Research\Apr08-SensBase.lst 15:0,0,0,0,0,0 19:75,3 27:0, 4:Time 5:Climbing Time[Model] 24:0 25:300 26:300 6:a1 6:a2 6:a3 6:Crr 6:Fixed 6:Myo 6:OrdN 6:PfR 6:rg1 6:rg2 6:rg3 6:rg4 6:Rgr