scml.scml2020.world =================== .. py:module:: scml.scml2020.world .. autoapi-nested-parse:: Implements the world class for the SCML2020 world Classes ------- .. autoapisummary:: scml.scml2020.world.AWI scml.scml2020.world.Failure scml.scml2020.world.SCML2020World scml.scml2020.world.SCML2021World scml.scml2020.world.SCML2022World scml.scml2020.world.SCML2023World scml.scml2020.world.SCML2024World Module Contents --------------- .. py:class:: AWI(world: negmas.situated.world.World, agent: negmas.situated.agent.Agent) Bases: :py:obj:`negmas.AgentWorldInterface` The Agent SCML2020World Interface for SCML2020 world. This class contains all the methods needed to access the simulation to extract information which are divided into 5 groups: Static World Information: Information about the world and the agent that does not change over time. These include: A. Market Information: - *n_products*: Number of products in the production chain. - *n_processes*: Number of processes in the production chain. - *n_competitors*: Number of other factories on the same production level. - *all_suppliers*: A list of all suppliers by product. - *all_consumers*: A list of all consumers by product. - *catalog_prices*: A list of the catalog prices (by product). - *inputs*: Inputs to every manufacturing process. - *outputs*: Outputs to every manufacturing process. - *is_system*: Is the given system ID corresponding to a system agent? - *is_bankrupt*: Is the given agent bankrupt (None asks about self)? - *current_step*: Current simulation step (inherited from `negmas.situated.AgentWorldInterface` ). - *n_steps*: Number of simulation steps (inherited from `negmas.situated.AgentWorldInterface` ). - *relative_time*: fraction of the simulation completed (inherited from `negmas.situated.AgentWorldInterface`). - *settings*: The system settings (inherited from `negmas.situated.AgentWorldInterface` ). B. Agent Information: - *profile*: Gives the agent profile including its production cost, number of production lines, input product index, mean of its delivery penalties, mean of its disposal costs, standard deviation of its shortfall penalties and standard deviation of its disposal costs. See `OneShotProfile` for full description. This information is private information and no other agent knows it. - *n_lines*: the number of production lines in the factory (private information). - *is_first_level*: Is the agent in the first production level (i.e. it is an input agent that buys the raw material). - *is_last_level*: Is the agent in the last production level (i.e. it is an output agent that sells the final product). - *is_middle_level*: Is the agent neither a first level nor a last level agent - *my_input_product*: The input product to the factory controlled by the agent. - *my_output_product*: The output product from the factory controlled by the agent. - *my_input_products*: All input products of a factory controlled by the agent. Currently, it is always a list of one item. For future compatibility. - *my_output_products*: All output products of a factory controlled by the agent. Currently, it is always a list of one item. For future compatibility. - *available_for_production*: Returns the line-step slots available for production. - *level*: The production level which is numerically the same as the input product. - *my_suppliers*: A list of IDs for all suppliers to the agent (i.e. agents that can sell the input product of the agent). - *my_consumers*: A list of IDs for all consumers to the agent (i.e. agents that can buy the output product of the agent). - *penalties_scale*: The scale at which to calculate disposal cost/delivery penalties. "trading" and "catalog" mean trading and catalog prices. "unit" means the contract's unit price while "none" means that disposal cost/shortfall penalty are absolute. - *n_input_negotiations*: Number of negotiations with suppliers. - *n_output_negotiations*: Number of negotiations with consumers. - *state*: The full state of the agent ( `FactoryState` ). - *current_balance*: The current balance of the agent - *current_inventory*: The current inventory of the agent (quantity per product) Dynamic World Information: Information about the world and the agent that changes over time. A. Market Information: - *trading_prices*: The trading prices of all products. This information is only available if `publish_trading_prices` is set in the world. - *exogenous_contract_summary*: A list of n_products tuples each giving the total quantity and average price of exogenous contracts for a product. This information is only available if `publish_exogenous_summary` is set in the world. B. Other Agents' Information: - *reports_of_agent*: Gives all past financial reports of a given agent. See `FinancialReport` for details. - *reports_at_step*: Gives all reports of all agents at a given step. See `FinancialReport` for details. C. Current Negotiations Information: - *current_input_issues*: The current issues for all negotiations to buy the input product of the agent. If the agent is at level zero, this will be empty. - *current_output_issues*: The current issues for all negotiations to buy the output product of the agent. If the agent is at level n_products - 1, this will be empty. D. Agent Information: - *spot_market_quantity*: The quantity the agent bought from the spot market at a given step - *spot_market_loss*: The spot market loss for the agent. Actions: A. Negotiation Control: - *request_negotiations*: Requests a set of negotiations controlled by a single controller. - *request_negotiation*: Requests a negotiation controlled by a single negotiator. B. Production Control: - *schedule_production*: Schedules production using one of the predefined scheduling strategies. - *order_production*: Orders production directly for the current step. - *set_commands*: Sets production commands directly on the factory. - *cancel_production*: Cancels a scheduled production command. Services (All inherited from `negmas.situated.AgentWorldInterface`): - *logdebug/loginfo/logwarning/logerror*: Logs to the world log at the given log level. - *logdebug_agent/loginf_agnet/...*: Logs to the agent specific log at the given log level. - *bb_query*: Queries the bulletin-board. - *bb_read*: Read a section of the bulletin-board. .. py:method:: request_negotiations(is_buy: bool, product: int, quantity: Union[int, Tuple[int, int]], unit_price: Union[int, Tuple[int, int]], time: Union[int, Tuple[int, int]], controller: Optional[negmas.SAOController] = None, negotiators: List[negmas.Negotiator] = None, partners: List[str] = None, extra: Dict[str, Any] = None, copy_partner_id=True) -> bool Requests a negotiation :param is_buy: If True the negotiation is about buying otherwise selling. :param product: The product to negotiate about :param quantity: The minimum and maximum quantities. Passing a single value q is equivalent to passing (q,q) :param unit_price: The minimum and maximum unit prices. Passing a single value u is equivalent to passing (u,u) :param time: The minimum and maximum delivery step. Passing a single value t is equivalent to passing (t,t) :param controller: The controller to manage the complete set of negotiations :param negotiators: An optional list of negotiators to use for negotiating with the given partners (in the same order). :param partners: ID of all the partners to negotiate with. :param extra: Extra information accessible through the negotiation annotation to the caller :param copy_partner_id: If true, the partner ID will be copied to the negotiator ID :returns: `True` if the partner accepted and the negotiation is ready to start Remarks: - You can either use controller or negotiators. One of them must be None. - All negotiations will use the following issues **in order**: quantity, time, unit_price - Negotiations with bankrupt agents or on invalid products (see next point) will be automatically rejected - Valid products for a factory are the following (any other products are not valid): 1. Buying an input product (i.e. product $\in$ `my_input_products` ) and an output product if the world settings allows it (see `allow_buying_output`) 1. Selling an output product (i.e. product $\in$ `my_output_products` ) and an input product if the world settings allows it (see `allow_selling_input`) .. py:method:: request_negotiation(is_buy: bool, product: int, quantity: Union[int, Tuple[int, int]], unit_price: Union[int, Tuple[int, int]], time: Union[int, Tuple[int, int]], partner: str, negotiator: negmas.SAONegotiator, extra: Dict[str, Any] = None) -> bool Requests a negotiation :param is_buy: If True the negotiation is about buying otherwise selling. :param product: The product to negotiate about :param quantity: The minimum and maximum quantities. Passing a single value q is equivalent to passing (q,q) :param unit_price: The minimum and maximum unit prices. Passing a single value u is equivalent to passing (u,u) :param time: The minimum and maximum delivery step. Passing a single value t is equivalent to passing (t,t) :param partner: ID of the partner to negotiate with. :param negotiator: The negotiator to use for this negotiation (if the partner accepted to negotiate) :param extra: Extra information accessible through the negotiation annotation to the caller :returns: `True` if the partner accepted and the negotiation is ready to start Remarks: - All negotiations will use the following issues **in order**: quantity, time, unit_price - Negotiations with bankrupt agents or on invalid products (see next point) will be automatically rejected - Valid products for a factory are the following (any other products are not valid): 1. Buying an input product (i.e. product $\in$ `my_input_products` ) and an output product if the world settings allows it (see `allow_buying_output`) 1. Selling an output product (i.e. product $\in$ `my_output_products` ) and an input product if the world settings allows it (see `allow_selling_input`) .. py:method:: schedule_production(process: int, repeats: int, step: Union[int, Tuple[int, int]] = ANY_STEP, line: int = ANY_LINE, override: bool = True, method: str = 'latest', partial_ok: bool = False) -> Tuple[numpy.ndarray, numpy.ndarray] Orders the factory to run the given process at the given line at the given step :param process: The process to run :param repeats: How many times to repeat the process :param step: The simulation step or a range of steps. The special value ANY_STEP gives the factory the freedom to schedule production at any step in the present or future. :param line: The production line. The special value ANY_LINE gives the factory the freedom to use any line :param override: Whether to override existing production commands or not :param method: When to schedule the command if step was set to a range. Options are latest, earliest :param partial_ok: If true, allows partial scheduling :returns: Tuple[int, int] giving the steps and lines at which production is scheduled. Remarks: - The step cannot be in the past. Production can only be ordered for current and future steps - ordering production of process -1 is equivalent of `cancel_production` only if both step and line are given .. py:method:: order_production(process: int, steps: numpy.ndarray, lines: numpy.ndarray) -> None Orders production of the given process :param process: The process to run :param steps: The time steps to run the process at as an np.ndarray :param lines: The corresponding lines to run the process at Remarks: - len(steps) must equal len(lines) - No checks are done in this function. It is expected to be used after calling `available_for_production` .. py:method:: available_for_production(repeats: int, step: Union[int, Tuple[int, int]] = ANY_STEP, line: int = ANY_LINE, override: bool = True, method: str = 'latest') -> Tuple[numpy.ndarray, numpy.ndarray] Finds available times and lines for scheduling production. :param repeats: How many times to repeat the process :param step: The simulation step or a range of steps. The special value ANY_STEP gives the factory the freedom to schedule production at any step in the present or future. :param line: The production line. The special value ANY_LINE gives the factory the freedom to use any line :param override: Whether to override any existing commands at that line at that time. :param method: When to schedule the command if step was set to a range. Options are latest, earliest, all :returns: Tuple[np.ndarray, np.ndarray] The steps and lines at which production is scheduled. Remarks: - You cannot order production in the past or in the current step - Ordering production, will automatically update inventory and balance for all simulation steps assuming that this production will be carried out. At the indicated `step` if production was not possible (due to insufficient funds or insufficient inventory of the input product), the predictions for the future will be corrected. .. py:method:: set_commands(commands: numpy.ndarray, step: int = -1) -> None Sets the production commands for all lines in the given step :param commands: n_lines vector of commands. A command is either a process number to run or `NO_COMMAND` to keep the line idle :param step: The step to set the commands at. If < 0, it means current step .. py:method:: cancel_production(step: int, line: int) -> bool Cancels any production commands on that line at this step :param step: The step to cancel production at (must be in the future). :param line: The production line :returns: success/failure Remarks: - The step cannot be in the past or the current step. Cancellation can only be ordered for future steps .. py:property:: trading_prices :type: numpy.ndarray Returns the current trading prices of all products .. py:property:: exogenous_contract_summary :type: List[Tuple[int, int]] The exogenous contracts in the current step for all products :returns: A list of tuples giving the total quantity and total price of all revealed exogenous contracts of all products at the current step. .. py:property:: allow_zero_quantity :type: bool Does negotiations allow zero quantity? .. py:property:: state :type: scml.scml2020.common.FactoryState Receives the factory state .. py:property:: current_balance Current balance of the agent .. py:property:: current_inventory Current inventory of the agent .. py:method:: reports_of_agent(aid: str) -> Dict[int, scml.scml2020.common.FinancialReport] Returns a dictionary mapping time-steps to financial reports of the given agent .. py:method:: reports_at_step(step: int) -> Dict[str, scml.scml2020.common.FinancialReport] Returns a dictionary mapping agent ID to its financial report for the given time-step .. py:property:: profile :type: scml.scml2020.common.FactoryProfile Gets the profile (static private information) associated with the agent .. py:property:: all_suppliers :type: List[List[str]] Returns a list of agent IDs for all suppliers for every product .. py:property:: all_consumers :type: List[List[str]] Returns a list of agent IDs for all consumers for every product .. py:property:: inputs :type: numpy.ndarray Returns the number of inputs to every production process .. py:property:: outputs :type: numpy.ndarray Returns the number of outputs to every production process .. py:property:: n_competitors :type: int Returns the number of factories/agents in the same production level .. py:property:: my_input_product :type: int Returns a list of products that are inputs to at least one process the agent can run .. py:property:: my_output_product :type: int Returns a list of products that are outputs to at least one process the agent can run .. py:property:: my_input_products :type: numpy.ndarray Returns a list of products that are inputs to at least one process the agent can run .. py:property:: my_output_products :type: numpy.ndarray Returns a list of products that are outputs to at least one process the agent can run .. py:property:: my_suppliers :type: List[str] Returns a list of IDs for all of the agent's suppliers (agents that can supply at least one product it may need). Remarks: - If the agent have multiple input products, suppliers of a specific product $p$ can be found using: **self.all_suppliers[p]**. .. py:property:: my_consumers :type: List[str] Returns a list of IDs for all the agent's consumers (agents that can consume at least one product it may produce). Remarks: - If the agent have multiple output products, consumers of a specific product $p$ can be found using: **self.all_consumers[p]**. .. py:property:: n_lines :type: int The number of lines in the corresponding factory. You can read `state` to get this among other information .. py:property:: catalog_prices :type: numpy.ndarray Returns the catalog prices of all products .. py:property:: n_products :type: int Number of products in the world .. py:property:: n_processes :type: int Returns the number of processes in the system .. py:property:: is_first_level Whether this agent is in the first production level .. py:property:: is_last_level Whether this agent is in the last production level .. py:property:: level The production level which is the index of the process for this factory (or the index of its input product) .. py:property:: is_middle_level Whether this agent is in neither in the first nor in the last level .. py:method:: is_system(aid: str) -> bool Checks whether an agent is a system agent or not :param aid: Agent ID .. py:method:: is_bankrupt(aid: Optional[str] = None) -> bool Checks whether the agent is bankrupt :param aid: Agent ID (None means self) .. py:method:: spot_market_quantity(step: Optional[int]) -> int The quantity bought by the agent from the spot market at the given step. :param step: The simulation step (day) Remarks: If step is `None`, the current step will be used .. py:method:: spot_market_loss(step: Optional[int]) -> int The spot market loss of the agent at the given step. :param step: The simulation step (day) Remarks: If step is `None`, the current step will be used .. py:class:: Failure A production failure .. py:attribute:: __slots__ :value: ['is_inventory', 'line', 'step', 'process'] .. py:attribute:: is_inventory :type: bool True if the cause of failure was insufficient inventory. If False, the cause was insufficient funds. Note that if both conditions were true, only insufficient funds (is_inventory=False) will be reported. .. py:attribute:: line :type: int The line at which the failure happened .. py:attribute:: step :type: int The step at which the failure happened .. py:attribute:: process :type: int The process that failed to execute .. py:class:: SCML2020World(process_inputs: numpy.ndarray, process_outputs: numpy.ndarray, catalog_prices: numpy.ndarray, profiles: list[scml.scml2020.common.FactoryProfile], agent_types: list[type[scml.scml2020.agent.SCML2020Agent]], agent_params: list[dict[str, Any]] | None = None, exogenous_contracts: Collection[scml.scml2020.common.ExogenousContract] = (), initial_balance: numpy.ndarray | tuple[int, int] | int = 1000, allow_buying_output=False, allow_selling_input=False, catalog_quantities: int | numpy.ndarray = 50, buy_missing_products=True, borrow_on_breach=True, bankruptcy_limit=0.0, liquidation_rate=1.0, spot_market_global_loss=0.3, interest_rate=0.05, financial_report_period: int = 5, compensation_fraction: float = 1.0, compensate_immediately=False, compensate_before_past_debt=True, exogenous_horizon: int | None = None, exogenous_force_max: bool = False, production_confirm=False, production_buy_missing=False, production_no_borrow=True, production_no_bankruptcy=False, production_penalty=0.15, compact=False, no_logs=False, n_steps=1000, time_limit=60 * 90, neg_n_steps=20, neg_time_limit=2 * 60, neg_step_time_limit=60, negotiation_speed=21, negotiation_quota_per_step=None, negotiation_quota_per_simulation=float('inf'), n_concurrent_negs_between_partners=float('inf'), shuffle_negotiations=False, end_negotiation_on_refusal_to_propose=True, trading_price_discount=0.9, spot_discount=0.9, spot_multiplier=0.05, signing_delay=0, force_signing=False, batch_signing=True, name: str = None, publish_exogenous_summary=True, publish_trading_prices=True, agent_name_reveals_position: bool = True, agent_name_reveals_type: bool = True, inventory_valuation_trading: float = 0.5, inventory_valuation_catalog: float = 0.0, **kwargs) Bases: :py:obj:`negmas.situated.TimeInAgreementMixin`, :py:obj:`negmas.situated.World` A Supply Chain SCML2020World simulation as described for the SCML league of ANAC @ IJCAI 2020. :param process_inputs: An n_processes vector specifying the number of inputs from each product needed to execute each process. :param process_outputs: An n_processes vector specifying the number of inputs from each product generated by executing each process. :param catalog_prices: An n_products vector (i.e. n_processes+1 vector) giving the catalog price of all products :param profiles: An n_agents list of `FactoryProfile` objects specifying the private profile of the factory associated with each agent. :param agent_types: An n_agents list of strings/ `SCML2020Agent` classes specifying the type of each agent :param agent_params: An n_agents dictionaries giving the parameters of each agent :param initial_balance: The initial balance in each agent's wallet. All agents will start with this same value. :param allow_selling_input: Allows agents to sell their input product(s) through negotiation :param allow_buying_output: Allows agents to buy their output product(s) through negotiation :param catalog_quantities: The quantities in the past for which catalog_prices are the average unit prices. This is used when updating the trading prices. If set to zero then the trading price will follow the market price and will not use the catalog_price (except for products that are never sold in the market for which the trading price will take the default value of the catalog price). If set to a large value (e.g. 10000), the price at which a product is sold will not affect the trading price :param spot_market_global_loss: Buying from the spot market will cost trading-price * (1+`spot_market_global_loss) and selling to it will cost trading-price / (1+ spot_market_global_loss) for agents with unit spot-market-loss-multiplier :param financial_report_period: The number of steps between financial reports. If < 1, it is a fraction of n_steps :param borrow_on_breach: If true, agents will be forced to borrow money on breach as much as possible to honor the contract :param interest_rate: The interest at which loans grow over time (it only affect a factory when its balance is negative) :param bankruptcy_limit: The maximum amount that be be borrowed (including interest). The balance of any factory cannot go lower than - borrow_limit or the agent will go bankrupt immediately :param liquidation_rate: The rate at which future contracts get liquidated when an agent gets bankrupt. It should be between zero and one. :param compensation_fraction: Fraction of a contract to be compensated (at most) if a partner goes bankrupt. Notice that this fraction is not guaranteed because the bankrupt agent may not have enough assets to pay all of its standing contracts to this level of compensation. In such cases, a smaller fraction will be used. :param compensate_immediately: If true, compensation will happen immediately when an agent goes bankrupt and in in money. This means that agents with contracts involving the bankrupt agent will just have these contracts be nullified and receive monetary compensation immediately . If false, compensation will not happen immediately but at the contract execution time. In this case, agents with contracts involving the bankrupt agent will be informed of the compensation fraction (instead of the compensation money) at the time of bankruptcy and will receive the compensation in kind (money if they are sellers and products if they are buyers) at the normal execution time of the contract. In the special case of no-compensation (i.e. `compensation_fraction` is zero or the bankrupt agent has no assets), the two options will behave similarity. :param compensate_before_past_debt: If true, then compensations will be paid before past debt is considered, otherwise, the money from liquidating bankrupt agents will first be used to pay past debt then whatever remains will be used for compensation. Notice that in all cases, the trigger of bankruptcy will be paid before compensation and past debts. :param exogenous_horizon: The horizon for revealing external contracts :param exogenous_force_max: If true, exogenous contracts are forced to be signed independent of the setting of `force_signing` :param production_no_borrow: If true, agents will not borrow if they fail to satisfy its production need to execute a scheduled production command :param production_no_bankruptcy: If true, agents will not go bankrupt because of an production related transaction. :param production_penalty: The penalty paid when buying from spot-market to satisfy production needs :param production_confirm: If true, the factory will confirm running processes at every time-step just before running them by calling `confirm_production` on the agent controlling it. :param compact: If True, no logs will be kept and the whole simulation will use a smaller memory footprint :param n_steps: Number of simulation steps (can be considered as days). :param time_limit: Total time allowed for the complete simulation in seconds. :param neg_n_steps: Number of negotiation steps allowed for all negotiations. :param neg_time_limit: Total time allowed for a complete negotiation in seconds. :param neg_step_time_limit: Total time allowed for a single step of a negotiation. in seconds. :param negotiation_speed: The number of negotiation steps that pass in every simulation step. If 0, negotiations will be guaranteed to finish within a single simulation step :param signing_delay: The number of simulation steps to pass between a contract is concluded and signed :param name: The name of the simulations :param \*\*kwargs: Other parameters that are passed directly to `SCML2020World` constructor. .. py:attribute:: publish_exogenous_summary :value: True .. py:attribute:: publish_trading_prices :value: True .. py:attribute:: allow_buying_output :value: False .. py:attribute:: allow_selling_input :value: False .. py:attribute:: exogenous_horizon :value: None .. py:attribute:: buy_missing_products :value: True .. py:attribute:: production_buy_missing :value: False .. py:attribute:: liquidation_rate :value: 1.0 .. py:attribute:: trading_price_discount :value: 0.9 .. py:attribute:: spot_discount :value: 0.9 .. py:attribute:: spot_multiplier :value: 0.05 .. py:attribute:: catalog_quantities :value: 50 .. py:attribute:: inventory_valuation_trading :value: 0.5 .. py:attribute:: inventory_valuation_catalog :value: 0.0 .. py:attribute:: n_concurrent_negs_between_partners .. py:attribute:: compact :value: False .. py:attribute:: spot_market_global_loss :value: 0.3 .. py:attribute:: production_no_borrow :value: True .. py:attribute:: production_no_bankruptcy :value: False .. py:attribute:: production_penalty :value: 0.15 .. py:attribute:: compensation_fraction :value: 1.0 .. py:attribute:: profiles .. py:attribute:: catalog_prices .. py:attribute:: process_inputs .. py:attribute:: process_outputs .. py:attribute:: n_products .. py:attribute:: n_processes .. py:attribute:: borrow_on_breach :value: True .. py:attribute:: interest_rate :value: 0.05 .. py:attribute:: exogenous_force_max :value: False .. py:attribute:: compensate_before_past_debt :value: True .. py:attribute:: confirm_production :value: False .. py:attribute:: financial_reports_period :value: 5 .. py:attribute:: compensate_immediately :value: False .. py:attribute:: bankruptcy_limit :value: -0.0 .. py:attribute:: agent_types .. py:attribute:: agent_params .. py:attribute:: agent_unique_types .. py:attribute:: factories .. py:attribute:: a2f .. py:attribute:: afp .. py:attribute:: i2a :value: [] .. py:attribute:: i2f .. py:attribute:: breach_prob .. py:attribute:: _breach_level .. py:attribute:: agent_n_contracts .. py:attribute:: suppliers :type: list[list[str]] .. py:attribute:: consumers :type: list[list[str]] .. py:attribute:: agent_processes :type: dict[str, list[int]] .. py:attribute:: agent_inputs :type: dict[str, list[int]] .. py:attribute:: agent_outputs :type: dict[str, list[int]] .. py:attribute:: agent_consumers :type: dict[str, list[str]] .. py:attribute:: agent_suppliers :type: dict[str, list[str]] .. py:attribute:: agent_profiles :type: dict[str, Any] .. py:attribute:: initial_balances :type: dict[str, Any] .. py:attribute:: _n_production_failures :value: 0 .. py:attribute:: __n_nullified :value: 0 .. py:attribute:: __n_bankrupt :value: 0 .. py:attribute:: penalties :value: 0 .. py:attribute:: compensation_balance :value: 0 .. py:attribute:: compensation_records :type: dict[str, list[CompensationRecord]] .. py:attribute:: exogenous_contracts :type: dict[int:list[Contract]] .. py:attribute:: compensation_factory .. py:attribute:: _agent_output .. py:attribute:: _agent_input .. py:attribute:: _traded_quantity .. py:attribute:: _real_price .. py:attribute:: _sold_quantity .. py:attribute:: _trading_price .. py:attribute:: _betas .. py:attribute:: _betas_sum .. py:attribute:: _spot_quantity .. py:attribute:: _alphas .. py:attribute:: _agent_spot_loss .. py:attribute:: _agent_spot_quantity .. py:attribute:: _registered_negs :type: dict[tuple[str], int] .. py:attribute:: exogenous_contracts_summary :value: None .. py:method:: generate(agent_types: list[type[scml.scml2020.agent.SCML2020Agent] | str], agent_params: list[dict[str, Any]] | None = None, agent_processes: list[int] | None = None, n_steps: tuple[int, int] | int = (50, 200), n_processes: tuple[int, int] | int = (2, 4), n_lines: numpy.ndarray | tuple[int, int] | int = 10, n_agents_per_process: numpy.ndarray | tuple[int, int] | int = (2, 4), process_inputs: numpy.ndarray | tuple[int, int] | int = 1, process_outputs: numpy.ndarray | tuple[int, int] | int = 1, production_costs: numpy.ndarray | tuple[int, int] | int = (1, 4), profit_means: numpy.ndarray | tuple[float, float] | float = (0.15, 0.2), profit_stddevs: numpy.ndarray | tuple[float, float] | float = 0.001, max_productivity: numpy.ndarray | tuple[float, float] | float = 1.0, initial_balance: numpy.ndarray | tuple[int, int] | int | None = None, cost_increases_with_level=True, equal_exogenous_supply=False, equal_exogenous_sales=False, exogenous_supply_predictability: tuple[float, float] | float = (0.6, 0.9), exogenous_sales_predictability: tuple[float, float] | float = (0.6, 0.9), exogenous_control: tuple[float, float] | float = (0.2, 0.8), cash_availability: tuple[float, float] | float = (1.5, 2.5), force_signing=False, profit_basis=np.max, horizon: tuple[float, float] | float = (0.2, 0.5), inventory_valuation_trading: numpy.ndarray | tuple[float, float] | float = 0.5, inventory_valuation_catalog: numpy.ndarray | tuple[float, float] | float = 0.0, random_agent_types: bool = False, cost_relativity: float = 1.0, exogenous_generation_method='profitable', exogenous_supply_surplus: tuple[float, float] | float = 0.0, exogenous_sales_surplus: tuple[float, float] | float = 0.0, run_extra_checks: bool = True, **kwargs) -> dict[str, Any] :classmethod: Generates the configuration for a world :param agent_types: All agent types :param agent_params: Agent parameters used to initialize them :param n_steps: Number of simulation steps :param n_processes: Number of processes in the production chain :param n_lines: Number of lines per factory :param process_inputs: Number of input units per process :param process_outputs: Number of output units per process :param production_costs: Production cost per factory :param profit_means: Mean profitability per production level (i.e. process). :param profit_stddevs: Std. Dev. of the profitability of every level (i.e. process). :param inventory_valuation_catalog: The fraction of catalog price to value items at the end. :param inventory_valuation_trading: The fraction of trading price to value items at the end. :param max_productivity: Maximum possible productivity per level (i.e. process). :param initial_balance: The initial balance of all agents :param n_agents_per_process: Number of agents per process :param agent_processes: The process for each agent. If not `None` , it will override `n_agents_per_process` and must be a list/tuple of the same length as `agent_types` . Morevoer, `random_agent_types` must be False in this case :param cost_increases_with_level: If true, production cost will be higher for processes nearer to the final product. :param profit_basis: The statistic used when controlling catalog prices by profit arguments. It can be np.mean, np.median, np.min, np.max or any Callable[[list[float]], float] and is used to summarize production costs at every level. :param horizon: The horizon used for revealing external supply/sales as a fraction of n_steps :param equal_exogenous_supply: If true, external supply will be distributed equally among all agents in the first layer :param equal_exogenous_sales: If true, external sales will be distributed equally among all agents in the last layer :param exogenous_supply_predictability: How predictable are exogenous supplies of each agent over time. 1.0 means that every agent will have the same quantity for all of its contracts over time. 0.0 means quantities per agent are completely random :param exogenous_sales_predictability: How predictable are exogenous supplies of each agent over time. 1.0 means that every agent will have the same quantity for all of its contracts over time. 0.0 means quantities per agent are completely random :param cash_availability: The fraction of the total money needs of the agent to work at maximum capacity that is available as `initial_balance` . This is only effective if `initial_balance` is set to `None` . :param force_signing: Whether to force contract signatures (exogenous contracts are treated in the same way). :param exogenous_control: How much control does the agent have over exogenous contract signing. Only effective if force_signing is False and use_exogenous_contracts is True :param random_agent_types: If True, the final agent types used by the generato wil always be sampled from the given types. If False, this random sampling will only happin if len(agent_types) != n_agents. :param cost_relativity: The exponent of production cost used to distribute contracts during generation :param method: The method used for world generation. Available methods are "profitable" and "guaranteed_profit" :param exogenous_supply_surplus: The surpolus exogenous supply contract quantity to add to the system as a fraction of the a fraction of the contracts generated by the given method. :param exogenous_sales_surplus: The surpolus exogenous sales contract quantity to add to the system as a fraction of the a fraction of the contracts generated by the given method. :param run_extra_checks: If given, the world generation method will check whether the genrated world "makes sense" given its internal criteria. May slow down world generation :param \*\*kwargs: :returns: world configuration as a dict[str, Any]. A world can be generated from this dict by calling SCML2020World(**d) Remarks: - There are two general ways to use this generator: 1. Pass `random_agent_types = True`, and pass `agent_types`, `agent_processes` to control placement of each agent in each level of the production graph. 2. Pass `random_agent_types = False` and pass `agent_types`, `n_agents_per_process` to make the system randomly place the specified number of agents in each production level - Most parameters (i.e. `process_inputs` , `process_outputs` , `n_agents_per_process` , `costs` ) can take a single value, a tuple of two values, or a list of values. If it has a single value, it is repeated for all processes/factories as appropriate. If it is a tuple of two numbers $(i, j)$, each process will take a number sampled from a uniform distribution supported on $[i, j]$ inclusive. If it is a list of values, of the length `n_processes` , it is used as it is otherwise, it is used to sample values for each process. .. py:method:: generate_guaranteed_profit(n_steps: int, n_lines: int, n_agents_per_process: int, process_of_agent: list[int], first_agent: list[int], last_agent: list[int], production_costs: list[int], exogenous_control: float, cash_availability: float, force_signing: bool, horizon: int, exogenous_supplies: list[int], max_productivity_process: list[float], max_productivity_agent: list[float], equal_exogenous_sales: bool, process_inputs: list[int], process_outputs: list[int], exogenous_sales_predictability: float, costs: numpy.ndarray, profit_stddevs_agent=list[float], profit_means_agent=list[float], initial_balance: numpy.ndarray | tuple[int, int] | int | None = None, cost_relativity: float = 1.0, profit_basis=np.max, inventory_valuation_trading: float = 0.5, inventory_valuation_catalog: float = 0.0, run_extra_checks=True) -> tuple[list[scml.scml2020.common.ExogenousContract], list[int], list[scml.scml2020.common.FactoryProfile], list[float], dict[str, Any]] :classmethod: Generates prices, contracts and profiles ensuring that all agents can profit and returning a set of explict contracts that can achieve this profit .. py:method:: generate_profitable(n_steps: int, n_lines: int, n_agents_per_process: int, process_of_agent: list[int], first_agent: list[int], last_agent: list[int], production_costs: list[int], exogenous_control: float, cash_availability: float, force_signing: bool, horizon: int, exogenous_supplies: list[int], max_productivity_process: list[float], max_productivity_agent: list[float], equal_exogenous_sales: bool, process_inputs: list[int], process_outputs: list[int], exogenous_sales_predictability: float, costs: numpy.ndarray, profit_stddevs_agent=list[float], profit_means_agent=list[float], initial_balance: numpy.ndarray | tuple[int, int] | int | None = None, cost_relativity: float = 1.0, profit_basis=np.max, inventory_valuation_trading: float = 0.5, inventory_valuation_catalog: float = 0.0, run_extra_checks: bool = True) -> tuple[list[scml.scml2020.common.ExogenousContract], list[int], list[scml.scml2020.common.FactoryProfile], list[float], dict[str, Any]] :classmethod: Generates the prices, contracts and profiles ensuring there is some possibility of profit in the market .. py:method:: get_private_state(agent: scml.scml2020.agent.SCML2020Agent) -> dict Reads the private state of the given agent .. py:method:: add_financial_report(agent: scml.scml2020.agent.SCML2020Agent, factory: scml.scml2020.factory.Factory, reports_agent, reports_time) -> None Records a financial report for the given agent in the agent indexed reports and time indexed reports :param agent: The agent :param factory: Its factory :param reports_agent: A dictionary of financial reports indexed by agent id :param reports_time: A dictionary of financial reports indexed by time Returns: .. py:method:: negs_between(a1, a2) .. py:method:: current_balance(agent_id: str) .. py:method:: can_negotiate(a1, a2) .. py:method:: simulation_step(stage) A single step of the simulation. :param stage: How many times so far was this method called within the current simulation step Remarks: - Using the stage parameter, it is possible to have `Operations` . `SimulationStep` several times with the list of operations while differentiating between these calls. .. py:method:: contract_size(contract: negmas.Contract) -> float Returns an estimation of the **activity level** associated with this contract. Higher is better :param contract: Returns: .. py:method:: contract_record(contract: negmas.Contract) -> dict[str, Any] Converts a contract to a record suitable for permanent storage .. py:method:: breach_record(breach: negmas.Breach) -> dict[str, Any] Converts a breach to a record suitable for storage during the simulation .. py:method:: execute_action(action: negmas.Action, agent: scml.scml2020.agent.SCML2020Agent, callback: Callable = None) -> bool Executes the given action by the given agent .. py:method:: post_step_stats() Called at the end of the simulation step to update all stats Kept for backward compatibility and will be dropped. Override `update_stats` ins .. py:method:: pre_step_stats() Called at the beginning of the simulation step to prepare stats or update them Kept for backward compatibility and will be dropped. Override `update_stats` instead .. py:property:: productivity :type: float Fraction of production lines occupied during the simulation .. py:method:: welfare(include_bankrupt: bool = False) -> float Total welfare of all agents .. py:method:: relative_welfare(include_bankrupt: bool = False) -> float | None Total welfare relative to expected value. Returns None if no expectation is found in self.info .. py:property:: relative_productivity :type: float | None Productivity relative to the expected value. Will return None if self.info does not have the expected productivity .. py:property:: bankruptcy_rate :type: float The fraction of factories that went bankrupt .. py:property:: num_bankrupt :type: float The fraction of factories that went bankrupt .. py:method:: order_contracts_for_execution(contracts: Collection[negmas.Contract]) -> Collection[negmas.Contract] Orders the contracts in a specific time-step that are about to be executed .. py:method:: _execute(product: int, q: int, p: int, u: int, buyer_factory: scml.scml2020.factory.Factory, seller_factory: scml.scml2020.factory.Factory, has_breaches: bool) Executes the contract .. py:method:: __register_contract(agent_id: str, level: float) -> None Registers execution of the contract in the agent's stats .. py:method:: record_bankrupt(factory: scml.scml2020.factory.Factory) -> None Records agent bankruptcy .. py:method:: on_contract_concluded(contract: negmas.Contract, to_be_signed_at: int) -> None Called to add a contract to the existing set of unsigned contract after it is concluded :param contract: The contract to add :param to_be_signed_at: The timestep at which the contract is to be signed Remarks: - By default this function just adds the contract to the set of contracts maintaned by the world. - You should ALWAYS call this function when overriding it. .. py:method:: is_valid_contact(contract: negmas.Contract) -> bool Checks whether a signed contract is valid .. py:method:: on_contract_signed(contract: negmas.Contract) -> bool Called to add a contract to the existing set of contract after it is signed :param contract: The contract to add :returns: True if everything went OK and False otherwise Remarks: - By default this function just adds the contract to the set of contracts maintaned by the world. - You should ALWAYS call this function when overriding it. .. py:method:: nullify_contract(contract: negmas.Contract, new_quantity: int) .. py:method:: __register_breach(agent_id: str, level: float, contract_total: float, factory: scml.scml2020.factory.Factory) -> int Registers a breach of the given level on the given agent. Assume that the contract is already added to the agent_contracts :param agent_id: The perpetrator of the breach :param level: The breach level :param contract_total: The total of the contract breached (quantity * unit_price) :param factory: The factory corresponding to the perpetrator :returns: If nonzero, the agent should go bankrupt and this amount taken from them .. py:method:: _spot_loss(aid: str) -> float .. py:method:: start_contract_execution(contract: negmas.Contract) -> set[negmas.Breach] | None Tries to execute the contract :param contract: :returns: The set of breaches committed if any. If there are no breaches return an empty set :rtype: Set[Breach] Remarks: - You must call super() implementation of this method before doing anything - It is possible to return None which indicates that the contract was nullified (i.e. not executed due to a reason other than an execution exeception). .. py:method:: complete_contract_execution(contract: negmas.Contract, breaches: list[negmas.Breach], resolution: negmas.Contract) -> None Called after breach resolution is completed for contracts for which some potential breaches occurred. :param contract: The contract considered. :param breaches: The list of potential breaches that was generated by `_execute_contract`. :param resolution: The agreed upon resolution Returns: .. py:method:: compensate(available: int, factory: scml.scml2020.factory.Factory) -> dict[str, list[tuple[negmas.Contract, int, int]]] Called by a factory when it is going bankrupt after liquidation :param available: The amount available from liquidation :param factory: The factory being bankrupted :returns: A mapping from agent ID to nullified contracts, the new quantity for them and compensation_money .. py:method:: scores(assets_multiplier_trading: float | None = None, assets_multiplier_catalog: float | None = None, assets_multiplier: float | None = None) -> dict[str, float] scores of all agents given the asset multiplier. :param assets_multiplier: a multiplier to multiply the assets with. .. py:property:: winners The winners of this world (factory managers with maximum wallet balance .. py:method:: trading_prices_for(discount: float = 1.0, condition='executed') -> numpy.ndarray Calculates the prices at which all products traded using an optional discount factor :param discount: A discount factor to treat older prices less importantly (exponential discounting). :param condition: The condition for contracts to consider. Possible values are executed, signed, concluded, nullified :returns: an n_products vector of trading prices .. py:property:: trading_prices .. py:property:: stats_df :type: pandas.DataFrame Returns a pandas data frame with the stats .. py:property:: contracts_df :type: pandas.DataFrame Returns a pandas data frame with the contracts .. py:property:: system_agents :type: list[scml.scml2020.agent.SCML2020Agent] Returns the two system agents .. py:property:: system_agent_names :type: list[str] Returns the names two system agents .. py:property:: non_system_agents :type: list[scml.scml2020.agent.SCML2020Agent] Returns all agents except system agents .. py:property:: non_system_agent_names :type: list[str] Returns names of all agents except system agents .. py:property:: agreement_fraction :type: float Fraction of negotiations ending in agreement and leading to signed contracts .. py:attribute:: system_agent_ids .. py:attribute:: non_system_agent_ids .. py:method:: draw(steps: tuple[int, int] | int | None = None, what: Collection[str] = DEFAULT_EDGE_TYPES, who: Callable[[negmas.Agent], bool] = None, where: Callable[[negmas.Agent], int | tuple[float, float]] = None, together: bool = True, axs: Collection[matplotlib.axis.Axis] = None, ncols: int = 4, figsize: tuple[int, int] = (15, 15), **kwargs) -> tuple[matplotlib.axis.Axis, networkx.Graph] | tuple[list[matplotlib.axis.Axis], list[networkx.Graph]] .. py:class:: SCML2021World(*args, **kwargs) Bases: :py:obj:`SCML2020World` A Supply Chain SCML2020World simulation as described for the SCML league of ANAC @ IJCAI 2020. :param process_inputs: An n_processes vector specifying the number of inputs from each product needed to execute each process. :param process_outputs: An n_processes vector specifying the number of inputs from each product generated by executing each process. :param catalog_prices: An n_products vector (i.e. n_processes+1 vector) giving the catalog price of all products :param profiles: An n_agents list of `FactoryProfile` objects specifying the private profile of the factory associated with each agent. :param agent_types: An n_agents list of strings/ `SCML2020Agent` classes specifying the type of each agent :param agent_params: An n_agents dictionaries giving the parameters of each agent :param initial_balance: The initial balance in each agent's wallet. All agents will start with this same value. :param allow_selling_input: Allows agents to sell their input product(s) through negotiation :param allow_buying_output: Allows agents to buy their output product(s) through negotiation :param catalog_quantities: The quantities in the past for which catalog_prices are the average unit prices. This is used when updating the trading prices. If set to zero then the trading price will follow the market price and will not use the catalog_price (except for products that are never sold in the market for which the trading price will take the default value of the catalog price). If set to a large value (e.g. 10000), the price at which a product is sold will not affect the trading price :param spot_market_global_loss: Buying from the spot market will cost trading-price * (1+`spot_market_global_loss) and selling to it will cost trading-price / (1+ spot_market_global_loss) for agents with unit spot-market-loss-multiplier :param financial_report_period: The number of steps between financial reports. If < 1, it is a fraction of n_steps :param borrow_on_breach: If true, agents will be forced to borrow money on breach as much as possible to honor the contract :param interest_rate: The interest at which loans grow over time (it only affect a factory when its balance is negative) :param bankruptcy_limit: The maximum amount that be be borrowed (including interest). The balance of any factory cannot go lower than - borrow_limit or the agent will go bankrupt immediately :param liquidation_rate: The rate at which future contracts get liquidated when an agent gets bankrupt. It should be between zero and one. :param compensation_fraction: Fraction of a contract to be compensated (at most) if a partner goes bankrupt. Notice that this fraction is not guaranteed because the bankrupt agent may not have enough assets to pay all of its standing contracts to this level of compensation. In such cases, a smaller fraction will be used. :param compensate_immediately: If true, compensation will happen immediately when an agent goes bankrupt and in in money. This means that agents with contracts involving the bankrupt agent will just have these contracts be nullified and receive monetary compensation immediately . If false, compensation will not happen immediately but at the contract execution time. In this case, agents with contracts involving the bankrupt agent will be informed of the compensation fraction (instead of the compensation money) at the time of bankruptcy and will receive the compensation in kind (money if they are sellers and products if they are buyers) at the normal execution time of the contract. In the special case of no-compensation (i.e. `compensation_fraction` is zero or the bankrupt agent has no assets), the two options will behave similarity. :param compensate_before_past_debt: If true, then compensations will be paid before past debt is considered, otherwise, the money from liquidating bankrupt agents will first be used to pay past debt then whatever remains will be used for compensation. Notice that in all cases, the trigger of bankruptcy will be paid before compensation and past debts. :param exogenous_horizon: The horizon for revealing external contracts :param exogenous_force_max: If true, exogenous contracts are forced to be signed independent of the setting of `force_signing` :param production_no_borrow: If true, agents will not borrow if they fail to satisfy its production need to execute a scheduled production command :param production_no_bankruptcy: If true, agents will not go bankrupt because of an production related transaction. :param production_penalty: The penalty paid when buying from spot-market to satisfy production needs :param production_confirm: If true, the factory will confirm running processes at every time-step just before running them by calling `confirm_production` on the agent controlling it. :param compact: If True, no logs will be kept and the whole simulation will use a smaller memory footprint :param n_steps: Number of simulation steps (can be considered as days). :param time_limit: Total time allowed for the complete simulation in seconds. :param neg_n_steps: Number of negotiation steps allowed for all negotiations. :param neg_time_limit: Total time allowed for a complete negotiation in seconds. :param neg_step_time_limit: Total time allowed for a single step of a negotiation. in seconds. :param negotiation_speed: The number of negotiation steps that pass in every simulation step. If 0, negotiations will be guaranteed to finish within a single simulation step :param signing_delay: The number of simulation steps to pass between a contract is concluded and signed :param name: The name of the simulations :param \*\*kwargs: Other parameters that are passed directly to `SCML2020World` constructor. .. py:method:: generate(*args, inventory_valuation_trading: numpy.ndarray | tuple[float, float] | float = (0.0, 0.5), horizon: tuple[float, float] | float = (0.2, 0.5), **kwargs) -> dict[str, Any] :classmethod: Generates the configuration for a world :param agent_types: All agent types :param agent_params: Agent parameters used to initialize them :param n_steps: Number of simulation steps :param n_processes: Number of processes in the production chain :param n_lines: Number of lines per factory :param process_inputs: Number of input units per process :param process_outputs: Number of output units per process :param production_costs: Production cost per factory :param profit_means: Mean profitability per production level (i.e. process). :param profit_stddevs: Std. Dev. of the profitability of every level (i.e. process). :param inventory_valuation_catalog: The fraction of catalog price to value items at the end. :param inventory_valuation_trading: The fraction of trading price to value items at the end. :param max_productivity: Maximum possible productivity per level (i.e. process). :param initial_balance: The initial balance of all agents :param n_agents_per_process: Number of agents per process :param agent_processes: The process for each agent. If not `None` , it will override `n_agents_per_process` and must be a list/tuple of the same length as `agent_types` . Morevoer, `random_agent_types` must be False in this case :param cost_increases_with_level: If true, production cost will be higher for processes nearer to the final product. :param profit_basis: The statistic used when controlling catalog prices by profit arguments. It can be np.mean, np.median, np.min, np.max or any Callable[[list[float]], float] and is used to summarize production costs at every level. :param horizon: The horizon used for revealing external supply/sales as a fraction of n_steps :param equal_exogenous_supply: If true, external supply will be distributed equally among all agents in the first layer :param equal_exogenous_sales: If true, external sales will be distributed equally among all agents in the last layer :param exogenous_supply_predictability: How predictable are exogenous supplies of each agent over time. 1.0 means that every agent will have the same quantity for all of its contracts over time. 0.0 means quantities per agent are completely random :param exogenous_sales_predictability: How predictable are exogenous supplies of each agent over time. 1.0 means that every agent will have the same quantity for all of its contracts over time. 0.0 means quantities per agent are completely random :param cash_availability: The fraction of the total money needs of the agent to work at maximum capacity that is available as `initial_balance` . This is only effective if `initial_balance` is set to `None` . :param force_signing: Whether to force contract signatures (exogenous contracts are treated in the same way). :param exogenous_control: How much control does the agent have over exogenous contract signing. Only effective if force_signing is False and use_exogenous_contracts is True :param random_agent_types: If True, the final agent types used by the generato wil always be sampled from the given types. If False, this random sampling will only happin if len(agent_types) != n_agents. :param cost_relativity: The exponent of production cost used to distribute contracts during generation :param method: The method used for world generation. Available methods are "profitable" and "guaranteed_profit" :param exogenous_supply_surplus: The surpolus exogenous supply contract quantity to add to the system as a fraction of the a fraction of the contracts generated by the given method. :param exogenous_sales_surplus: The surpolus exogenous sales contract quantity to add to the system as a fraction of the a fraction of the contracts generated by the given method. :param run_extra_checks: If given, the world generation method will check whether the genrated world "makes sense" given its internal criteria. May slow down world generation :param \*\*kwargs: :returns: world configuration as a dict[str, Any]. A world can be generated from this dict by calling SCML2020World(**d) Remarks: - There are two general ways to use this generator: 1. Pass `random_agent_types = True`, and pass `agent_types`, `agent_processes` to control placement of each agent in each level of the production graph. 2. Pass `random_agent_types = False` and pass `agent_types`, `n_agents_per_process` to make the system randomly place the specified number of agents in each production level - Most parameters (i.e. `process_inputs` , `process_outputs` , `n_agents_per_process` , `costs` ) can take a single value, a tuple of two values, or a list of values. If it has a single value, it is repeated for all processes/factories as appropriate. If it is a tuple of two numbers $(i, j)$, each process will take a number sampled from a uniform distribution supported on $[i, j]$ inclusive. If it is a list of values, of the length `n_processes` , it is used as it is otherwise, it is used to sample values for each process. .. py:class:: SCML2022World(*args, **kwargs) Bases: :py:obj:`SCML2021World` A Supply Chain SCML2020World simulation as described for the SCML league of ANAC @ IJCAI 2020. :param process_inputs: An n_processes vector specifying the number of inputs from each product needed to execute each process. :param process_outputs: An n_processes vector specifying the number of inputs from each product generated by executing each process. :param catalog_prices: An n_products vector (i.e. n_processes+1 vector) giving the catalog price of all products :param profiles: An n_agents list of `FactoryProfile` objects specifying the private profile of the factory associated with each agent. :param agent_types: An n_agents list of strings/ `SCML2020Agent` classes specifying the type of each agent :param agent_params: An n_agents dictionaries giving the parameters of each agent :param initial_balance: The initial balance in each agent's wallet. All agents will start with this same value. :param allow_selling_input: Allows agents to sell their input product(s) through negotiation :param allow_buying_output: Allows agents to buy their output product(s) through negotiation :param catalog_quantities: The quantities in the past for which catalog_prices are the average unit prices. This is used when updating the trading prices. If set to zero then the trading price will follow the market price and will not use the catalog_price (except for products that are never sold in the market for which the trading price will take the default value of the catalog price). If set to a large value (e.g. 10000), the price at which a product is sold will not affect the trading price :param spot_market_global_loss: Buying from the spot market will cost trading-price * (1+`spot_market_global_loss) and selling to it will cost trading-price / (1+ spot_market_global_loss) for agents with unit spot-market-loss-multiplier :param financial_report_period: The number of steps between financial reports. If < 1, it is a fraction of n_steps :param borrow_on_breach: If true, agents will be forced to borrow money on breach as much as possible to honor the contract :param interest_rate: The interest at which loans grow over time (it only affect a factory when its balance is negative) :param bankruptcy_limit: The maximum amount that be be borrowed (including interest). The balance of any factory cannot go lower than - borrow_limit or the agent will go bankrupt immediately :param liquidation_rate: The rate at which future contracts get liquidated when an agent gets bankrupt. It should be between zero and one. :param compensation_fraction: Fraction of a contract to be compensated (at most) if a partner goes bankrupt. Notice that this fraction is not guaranteed because the bankrupt agent may not have enough assets to pay all of its standing contracts to this level of compensation. In such cases, a smaller fraction will be used. :param compensate_immediately: If true, compensation will happen immediately when an agent goes bankrupt and in in money. This means that agents with contracts involving the bankrupt agent will just have these contracts be nullified and receive monetary compensation immediately . If false, compensation will not happen immediately but at the contract execution time. In this case, agents with contracts involving the bankrupt agent will be informed of the compensation fraction (instead of the compensation money) at the time of bankruptcy and will receive the compensation in kind (money if they are sellers and products if they are buyers) at the normal execution time of the contract. In the special case of no-compensation (i.e. `compensation_fraction` is zero or the bankrupt agent has no assets), the two options will behave similarity. :param compensate_before_past_debt: If true, then compensations will be paid before past debt is considered, otherwise, the money from liquidating bankrupt agents will first be used to pay past debt then whatever remains will be used for compensation. Notice that in all cases, the trigger of bankruptcy will be paid before compensation and past debts. :param exogenous_horizon: The horizon for revealing external contracts :param exogenous_force_max: If true, exogenous contracts are forced to be signed independent of the setting of `force_signing` :param production_no_borrow: If true, agents will not borrow if they fail to satisfy its production need to execute a scheduled production command :param production_no_bankruptcy: If true, agents will not go bankrupt because of an production related transaction. :param production_penalty: The penalty paid when buying from spot-market to satisfy production needs :param production_confirm: If true, the factory will confirm running processes at every time-step just before running them by calling `confirm_production` on the agent controlling it. :param compact: If True, no logs will be kept and the whole simulation will use a smaller memory footprint :param n_steps: Number of simulation steps (can be considered as days). :param time_limit: Total time allowed for the complete simulation in seconds. :param neg_n_steps: Number of negotiation steps allowed for all negotiations. :param neg_time_limit: Total time allowed for a complete negotiation in seconds. :param neg_step_time_limit: Total time allowed for a single step of a negotiation. in seconds. :param negotiation_speed: The number of negotiation steps that pass in every simulation step. If 0, negotiations will be guaranteed to finish within a single simulation step :param signing_delay: The number of simulation steps to pass between a contract is concluded and signed :param name: The name of the simulations :param \*\*kwargs: Other parameters that are passed directly to `SCML2020World` constructor. .. py:class:: SCML2023World(*args, **kwargs) Bases: :py:obj:`SCML2022World` A Supply Chain SCML2020World simulation as described for the SCML league of ANAC @ IJCAI 2020. :param process_inputs: An n_processes vector specifying the number of inputs from each product needed to execute each process. :param process_outputs: An n_processes vector specifying the number of inputs from each product generated by executing each process. :param catalog_prices: An n_products vector (i.e. n_processes+1 vector) giving the catalog price of all products :param profiles: An n_agents list of `FactoryProfile` objects specifying the private profile of the factory associated with each agent. :param agent_types: An n_agents list of strings/ `SCML2020Agent` classes specifying the type of each agent :param agent_params: An n_agents dictionaries giving the parameters of each agent :param initial_balance: The initial balance in each agent's wallet. All agents will start with this same value. :param allow_selling_input: Allows agents to sell their input product(s) through negotiation :param allow_buying_output: Allows agents to buy their output product(s) through negotiation :param catalog_quantities: The quantities in the past for which catalog_prices are the average unit prices. This is used when updating the trading prices. If set to zero then the trading price will follow the market price and will not use the catalog_price (except for products that are never sold in the market for which the trading price will take the default value of the catalog price). If set to a large value (e.g. 10000), the price at which a product is sold will not affect the trading price :param spot_market_global_loss: Buying from the spot market will cost trading-price * (1+`spot_market_global_loss) and selling to it will cost trading-price / (1+ spot_market_global_loss) for agents with unit spot-market-loss-multiplier :param financial_report_period: The number of steps between financial reports. If < 1, it is a fraction of n_steps :param borrow_on_breach: If true, agents will be forced to borrow money on breach as much as possible to honor the contract :param interest_rate: The interest at which loans grow over time (it only affect a factory when its balance is negative) :param bankruptcy_limit: The maximum amount that be be borrowed (including interest). The balance of any factory cannot go lower than - borrow_limit or the agent will go bankrupt immediately :param liquidation_rate: The rate at which future contracts get liquidated when an agent gets bankrupt. It should be between zero and one. :param compensation_fraction: Fraction of a contract to be compensated (at most) if a partner goes bankrupt. Notice that this fraction is not guaranteed because the bankrupt agent may not have enough assets to pay all of its standing contracts to this level of compensation. In such cases, a smaller fraction will be used. :param compensate_immediately: If true, compensation will happen immediately when an agent goes bankrupt and in in money. This means that agents with contracts involving the bankrupt agent will just have these contracts be nullified and receive monetary compensation immediately . If false, compensation will not happen immediately but at the contract execution time. In this case, agents with contracts involving the bankrupt agent will be informed of the compensation fraction (instead of the compensation money) at the time of bankruptcy and will receive the compensation in kind (money if they are sellers and products if they are buyers) at the normal execution time of the contract. In the special case of no-compensation (i.e. `compensation_fraction` is zero or the bankrupt agent has no assets), the two options will behave similarity. :param compensate_before_past_debt: If true, then compensations will be paid before past debt is considered, otherwise, the money from liquidating bankrupt agents will first be used to pay past debt then whatever remains will be used for compensation. Notice that in all cases, the trigger of bankruptcy will be paid before compensation and past debts. :param exogenous_horizon: The horizon for revealing external contracts :param exogenous_force_max: If true, exogenous contracts are forced to be signed independent of the setting of `force_signing` :param production_no_borrow: If true, agents will not borrow if they fail to satisfy its production need to execute a scheduled production command :param production_no_bankruptcy: If true, agents will not go bankrupt because of an production related transaction. :param production_penalty: The penalty paid when buying from spot-market to satisfy production needs :param production_confirm: If true, the factory will confirm running processes at every time-step just before running them by calling `confirm_production` on the agent controlling it. :param compact: If True, no logs will be kept and the whole simulation will use a smaller memory footprint :param n_steps: Number of simulation steps (can be considered as days). :param time_limit: Total time allowed for the complete simulation in seconds. :param neg_n_steps: Number of negotiation steps allowed for all negotiations. :param neg_time_limit: Total time allowed for a complete negotiation in seconds. :param neg_step_time_limit: Total time allowed for a single step of a negotiation. in seconds. :param negotiation_speed: The number of negotiation steps that pass in every simulation step. If 0, negotiations will be guaranteed to finish within a single simulation step :param signing_delay: The number of simulation steps to pass between a contract is concluded and signed :param name: The name of the simulations :param \*\*kwargs: Other parameters that are passed directly to `SCML2020World` constructor. .. py:class:: SCML2024World(*args, **kwargs) Bases: :py:obj:`SCML2022World` A Supply Chain SCML2020World simulation as described for the SCML league of ANAC @ IJCAI 2020. :param process_inputs: An n_processes vector specifying the number of inputs from each product needed to execute each process. :param process_outputs: An n_processes vector specifying the number of inputs from each product generated by executing each process. :param catalog_prices: An n_products vector (i.e. n_processes+1 vector) giving the catalog price of all products :param profiles: An n_agents list of `FactoryProfile` objects specifying the private profile of the factory associated with each agent. :param agent_types: An n_agents list of strings/ `SCML2020Agent` classes specifying the type of each agent :param agent_params: An n_agents dictionaries giving the parameters of each agent :param initial_balance: The initial balance in each agent's wallet. All agents will start with this same value. :param allow_selling_input: Allows agents to sell their input product(s) through negotiation :param allow_buying_output: Allows agents to buy their output product(s) through negotiation :param catalog_quantities: The quantities in the past for which catalog_prices are the average unit prices. This is used when updating the trading prices. If set to zero then the trading price will follow the market price and will not use the catalog_price (except for products that are never sold in the market for which the trading price will take the default value of the catalog price). If set to a large value (e.g. 10000), the price at which a product is sold will not affect the trading price :param spot_market_global_loss: Buying from the spot market will cost trading-price * (1+`spot_market_global_loss) and selling to it will cost trading-price / (1+ spot_market_global_loss) for agents with unit spot-market-loss-multiplier :param financial_report_period: The number of steps between financial reports. If < 1, it is a fraction of n_steps :param borrow_on_breach: If true, agents will be forced to borrow money on breach as much as possible to honor the contract :param interest_rate: The interest at which loans grow over time (it only affect a factory when its balance is negative) :param bankruptcy_limit: The maximum amount that be be borrowed (including interest). The balance of any factory cannot go lower than - borrow_limit or the agent will go bankrupt immediately :param liquidation_rate: The rate at which future contracts get liquidated when an agent gets bankrupt. It should be between zero and one. :param compensation_fraction: Fraction of a contract to be compensated (at most) if a partner goes bankrupt. Notice that this fraction is not guaranteed because the bankrupt agent may not have enough assets to pay all of its standing contracts to this level of compensation. In such cases, a smaller fraction will be used. :param compensate_immediately: If true, compensation will happen immediately when an agent goes bankrupt and in in money. This means that agents with contracts involving the bankrupt agent will just have these contracts be nullified and receive monetary compensation immediately . If false, compensation will not happen immediately but at the contract execution time. In this case, agents with contracts involving the bankrupt agent will be informed of the compensation fraction (instead of the compensation money) at the time of bankruptcy and will receive the compensation in kind (money if they are sellers and products if they are buyers) at the normal execution time of the contract. In the special case of no-compensation (i.e. `compensation_fraction` is zero or the bankrupt agent has no assets), the two options will behave similarity. :param compensate_before_past_debt: If true, then compensations will be paid before past debt is considered, otherwise, the money from liquidating bankrupt agents will first be used to pay past debt then whatever remains will be used for compensation. Notice that in all cases, the trigger of bankruptcy will be paid before compensation and past debts. :param exogenous_horizon: The horizon for revealing external contracts :param exogenous_force_max: If true, exogenous contracts are forced to be signed independent of the setting of `force_signing` :param production_no_borrow: If true, agents will not borrow if they fail to satisfy its production need to execute a scheduled production command :param production_no_bankruptcy: If true, agents will not go bankrupt because of an production related transaction. :param production_penalty: The penalty paid when buying from spot-market to satisfy production needs :param production_confirm: If true, the factory will confirm running processes at every time-step just before running them by calling `confirm_production` on the agent controlling it. :param compact: If True, no logs will be kept and the whole simulation will use a smaller memory footprint :param n_steps: Number of simulation steps (can be considered as days). :param time_limit: Total time allowed for the complete simulation in seconds. :param neg_n_steps: Number of negotiation steps allowed for all negotiations. :param neg_time_limit: Total time allowed for a complete negotiation in seconds. :param neg_step_time_limit: Total time allowed for a single step of a negotiation. in seconds. :param negotiation_speed: The number of negotiation steps that pass in every simulation step. If 0, negotiations will be guaranteed to finish within a single simulation step :param signing_delay: The number of simulation steps to pass between a contract is concluded and signed :param name: The name of the simulations :param \*\*kwargs: Other parameters that are passed directly to `SCML2020World` constructor.