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       "<style>.container { width:95% !important; }</style>"
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       "<IPython.core.display.HTML object>"
      ]
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   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# setup disply parameters\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pylab as plt\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "float_formatter = StrMethodFormatter('{x:0.03f}')\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:95% !important; }</style>\"))\n",
    "SMALL_SIZE = 14\n",
    "MEDIUM_SIZE = 16\n",
    "BIGGER_SIZE = 20\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "plt.rc('figure', figsize=(18, 6)) # set figure size"
   ]
  },
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   "source": [
    "from abc import abstractmethod, ABC\n",
    "from attr import define, field\n",
    "from random import random\n",
    "from negmas import Mechanism, MechanismRoundResult, Negotiator, PolyAspiration\n",
    "from negmas import MechanismState\n",
    "from negmas import NegotiatorMechanismInterface\n",
    "from typing import Callable, Tuple, Optional, List, Any, Dict\n",
    "from negmas import (\n",
    "    Outcome,\n",
    "    make_issue,\n",
    "    UtilityFunction,\n",
    "    LinearUtilityFunction,\n",
    "    ExpDiscountedUFun,\n",
    ")\n",
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987bec12",
   "metadata": {
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   "source": [
    "## Develop a new mechanism (protocol)\n",
    "\n",
    "Developing a new mechanism for NegMAS is as easy as overriding a single method. In most cases though, a base class for \n",
    "negotiators compatible with this protocol needs to be created.\n",
    "\n",
    "The algorithm for creating a new mechanism in `NegMAS` is simple:\n",
    "\n",
    "1. Create a new `Mechanism` class and override the `round()` method to implement a single round of your protocol. \n",
    "   Optionally override the `__init__` function to provide a constructor to initialize your mechanism. Define any\n",
    "   `requirements` of your mechanism that must be satisfied by negotiators using `add_requirements()` during construction\n",
    "    (i.e. in the `__init__` method).\n",
    "2. Create a new `Negotiator` class that has `abstractmethod(s)` for any methods called by your custom mechanism class\n",
    "   (usually in the `round()` method). This will be the base class of all negotiators compatible with this new \n",
    "   mechanism. Set any *capabilities* for this new negotiator base class in its `__init__` method using \n",
    "   `add_capabilities()` \n",
    "3. Optionally create a new `MechanismState` `dataclass` with any extra state information that the mechanism will pass to\n",
    "   negotiators and override the `extra_state()` method of your mechanism class to return a dict representation of this\n",
    "   extra state (you will see an example later in this tutorial). Moreover, you will need to change the `state_factory` \n",
    "   argument passed to the `Mechechanism` constructor to use this newly created state type. This step is optional \n",
    "   and is only needed if extra-state needs to be passed to negotiators through the `state` method. It t is recommended \n",
    "   to keep all variable publicly accessible information of the mechanism in its state variable. This way, the history of\n",
    "   the mechanism state will be accessible through the `history` property after the mechanism run.\n",
    "\n",
    "### Nash Bargaining Game (Stateless Mechanism)\n",
    "Let's start by developing one of the earliest negotiation protocols every proposed: **Nash Bargaining Game (1950)** .\n",
    "\n",
    "The Nash bargaining game is a single-step full-information bilateral negotiation with $\\Omega = [0, 1]^2$ and two \n",
    " utility functions ($\\tilde u_1, \\tilde u_2$) such that:\n",
    " - A (usually convex) feasible set of agreements $F$. A common example is to define $F$ as all the outcomes for which \n",
    "   the total utility received by negotiators is less than or equal to one:\n",
    "   \n",
    "   $$F = \\left\\{(\\omega_1, \\omega_2) | \\tilde u_2(\\omega_2) + \\tilde u_1(\\omega_1) \\le 1\\right\\}$$.\n",
    "   \n",
    " - A disagreement point $d \\equiv  \\tilde u_1(\\phi) + \\tilde u_2(\\phi) \\in \\Re^2$ which is the utility value received by the two players in case of disagreement (\n",
    "   reserved values) and $\\phi$ represents the disagreement.\n",
    " "
   ]
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   "source": [
    "class NashBargainingGame(Mechanism):\n",
    "    \"\"\"A mechanism representing the nash bargaining game\"\"\"\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        kwargs.update(dict(n_steps=1, max_n_agents=2, dynamic_entry=False))\n",
    "        super().__init__(**kwargs, issues=[make_issue((0.0, 1.0))])\n",
    "        self.add_requirements(dict(propose_for_self=True))\n",
    "        self.ufuns: List[UtilityFunction] = []\n",
    "\n",
    "    def add(\n",
    "        self,\n",
    "        negotiator: \"Negotiator\",\n",
    "        *,\n",
    "        preferences: Optional[\"Preferences\"] = None,\n",
    "        **kwargs,\n",
    "    ) -> Optional[bool]:\n",
    "        added = super().add(negotiator, preferences=preferences, role=None, **kwargs)\n",
    "        if added:\n",
    "            self.ufuns.append(self.negotiators[-1].ufun)\n",
    "\n",
    "    def is_feasible(self, outcome: Tuple[float]):\n",
    "        \"\"\"Tests feasibility of outcomes.\n",
    "\n",
    "        The default implementation tests that the of all utilities assigned to all negotiators is less than 1.0.\n",
    "        \"\"\"\n",
    "        return sum(u(outcome) for u in self.ufuns) <= (1.0 + 1e-3)\n",
    "\n",
    "    def round(self) -> MechanismRoundResult:\n",
    "        \"\"\"One round of the mechanism\"\"\"\n",
    "        if len(self.negotiators) != 2:\n",
    "            return MechanismRoundResult(\n",
    "                error=True,\n",
    "                error_details=f\"Got {len(self.negotiators)} negotiators!!\",\n",
    "                broken=True,\n",
    "            )\n",
    "        outcome = tuple(\n",
    "            n.propose_for_self(self.ufuns, i) for i, n in enumerate(self.negotiators)\n",
    "        )\n",
    "        if self.is_feasible(outcome):\n",
    "            return MechanismRoundResult(agreement=outcome)\n",
    "        return MechanismRoundResult()"
   ]
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   "source": [
    "The implementation is straight forward. We need to define what a round of the algorithm does (`round()` ). In this\n",
    "case we used `is_feasible()` to distinguish feasible and infeasible outcomes.\n",
    "\n",
    "We need now to develop a base class for negotiators compatible with this mechanism."
   ]
  },
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   "source": [
    "class NashBargainingNegotiator(Negotiator, ABC):\n",
    "    \"\"\"Base class of all negotiators capable of negotiating in the nash bargaining game\"\"\"\n",
    "\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.add_capabilities(dict(propose_for_self=True))\n",
    "\n",
    "    @abstractmethod\n",
    "    def propose_for_self(self, ufuns: List[UtilityFunction], my_indx: int) -> float:\n",
    "        \"\"\"Propose some outcome which is just a real number\"\"\"\n"
   ]
  },
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   "source": [
    "Note that any agreement with utilities that sum to the maximum possible value within the feasible space is a nash \n",
    "equilibrium to this game and is pareto-efficient.\n",
    "\n",
    "Based on the axioms used to represent rationality, there are three widely known equilibria for the Nash bargaining game:\n",
    "\n",
    "- Nash Point (1950): The point at which the product of surplus utility (above reservation value) of negotiators is maximized\n",
    " \n",
    "$$\\text{argmax}_{\\omega_1, \\omega_2} \\prod_{i=1}^2\\left(\\tilde u_i(\\omega_{i}) - \\tilde u_i(\\phi)\\right)$$\n",
    "\n",
    "- Kalai-Smorodinsky Point (1975): The pareto outcome with equal ratios of achieved  surplus utility and  maximum feasible \n",
    "surplus utility\n",
    "\n",
    "$$\\text{argmax}_{\\omega_1, \\omega_2 \\in F}\\left(\\omega_1+\\omega_2\\right) \n",
    "\\text{ s.t. }\n",
    "\\left(\\frac{\\tilde u_1(\\omega_1)-\\tilde u_1(\\phi)}{\\tilde u_2(\\omega_2)-\\tilde u_2(\\phi)} = \n",
    "\\frac{\\max_{v \\in F} \\left(\\tilde u_1(v)\\right)-\\tilde u_1(\\phi)}{\\max_{v \\in F} \n",
    "\\left(\\tilde u_2(v)\\right)-\\tilde u_2(\\phi)}\\right)$$\n",
    "\n",
    "- Kalai Point (1977): The pareto outcome maximizing the utility for the unfortunate player. Defining $P$ as the pareto\n",
    "front,\n",
    "\n",
    "$$\\text{argmax}_{\\omega_1, \\omega_2 \\in P} \\min_{i \\in \\{1,2\\}}\\left(\\tilde u_i(\\omega_{i}) - \\tilde u_i(\\phi)\\right)$$\n",
    "\n",
    "We will not implement a negotiator that plays he nash-point strategy with no prior knowledge of the utility function\n",
    "structure (i.e. it works for nonlinear nonconvex utility functions)."
   ]
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   "source": [
    "class NashNegotiator(NashBargainingNegotiator):\n",
    "    \"\"\"Implements Nash solution to the bargaining game\"\"\"\n",
    "\n",
    "    def propose_for_self(self, ufuns: List[UtilityFunction], my_indx: int) -> float:\n",
    "        objective = lambda f0: -(ufuns[0]((f0, 1.0 - f0)) - ufuns[0].reserved_value) * (\n",
    "            ufuns[1]((f0, 1.0 - f0)) - ufuns[1].reserved_value\n",
    "        )\n",
    "        while True:\n",
    "            result = minimize(objective, x0=[random()], bounds=[(0.0, 1.0)])\n",
    "            if result.success:\n",
    "                break\n",
    "        return float(result.x) if my_indx == 0 else 1.0 - float(result.x)"
   ]
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   "source": [
    "Now let's try our brand new mechanism and negotiator"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agreement: (0.4999999938325375, 0.5000000058232046)\n"
     ]
    }
   ],
   "source": [
    "m = NashBargainingGame()\n",
    "u1 = LinearUtilityFunction([1, 0], reserved_value=0.0, outcome_space=m.outcome_space)\n",
    "u2 = LinearUtilityFunction([0, 1], reserved_value=0.0, outcome_space=m.outcome_space)\n",
    "m.add(NashNegotiator(ufun=u1, name=\"a1\"))\n",
    "m.add(NashNegotiator(ufun=u2, name=\"a2\"))\n",
    "result = m.run()\n",
    "print(f\"Agreement: {result.agreement}\")"
   ]
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   "source": [
    "As expected, the two negotiators agreed on the expected nash point (0.5, 0.5) from the first trial.\n",
    "\n",
    "It is possible to see what happens at different other conditions. For example, how does the reservation value affect \n",
    "the outcome: "
   ]
  },
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    {
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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u1values, u2values = np.zeros(101), np.zeros(101)\n",
    "a1values, a2values = np.zeros(101), np.zeros(101)\n",
    "values = np.linspace(0.0, 1.0, 101, endpoint=True)\n",
    "for i, r in enumerate(values):\n",
    "    m = NashBargainingGame()\n",
    "    u1 = LinearUtilityFunction([1, 0], reserved_value=r, outcome_space=m.outcome_space)\n",
    "    u2 = LinearUtilityFunction(\n",
    "        [0, 1], reserved_value=0.0, outcome_space=m.outcome_space\n",
    "    )\n",
    "    m.add(NashNegotiator(ufun=u1, name=\"a1\"))\n",
    "    m.add(NashNegotiator(ufun=u2, name=\"a2\"))\n",
    "    result = m.run()\n",
    "    u1values[i] = u1(result.agreement)\n",
    "    u2values[i] = u2(result.agreement)\n",
    "    a1values[i], a2values[i] = result.agreement\n",
    "plt.subplot(211)\n",
    "plt.plot(values, u1values, label=\"First negotiator\")\n",
    "plt.plot(values, u2values, label=\"Second negotiator\")\n",
    "plt.plot(values, u1values + u2values, label=\"Welfare\")\n",
    "plt.ylabel(\"Utility received\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Reservation value for first negotiator\")\n",
    "plt.show()\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(values, a1values, label=\"First negotiator\")\n",
    "plt.plot(values, a2values, label=\"Second negotiator\")\n",
    "plt.ylabel(\"Agreement Reached\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Slope value for first negotiator\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c198d37e",
   "metadata": {
    "papermill": {
     "duration": 0.02922,
     "end_time": "2022-02-19T02:34:24.223797",
     "exception": false,
     "start_time": "2022-02-19T02:34:24.194577",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    " We can see that increasing the reservation value of a negotiator increases the utility it receives by the end of the \n",
    " negotiation proportionally.  \n",
    "\n",
    "What happens if the utility value of a negotiator had a different slope:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f7afa09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:24.307423Z",
     "iopub.status.busy": "2022-02-19T02:34:24.288486Z",
     "iopub.status.idle": "2022-02-19T02:34:24.984857Z",
     "shell.execute_reply": "2022-02-19T02:34:24.985523Z"
    },
    "papermill": {
     "duration": 0.735102,
     "end_time": "2022-02-19T02:34:24.985732",
     "exception": false,
     "start_time": "2022-02-19T02:34:24.250630",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "u1values, u2values = np.zeros(101), np.zeros(101)\n",
    "a1values, a2values = np.zeros(101), np.zeros(101)\n",
    "slopes = np.linspace(0.0, 1.0, 101, endpoint=True)\n",
    "for i, s in enumerate(slopes):\n",
    "    m = NashBargainingGame()\n",
    "    u1 = LinearUtilityFunction(\n",
    "        [s, 0.0], reserved_value=0.0, outcome_space=m.outcome_space\n",
    "    )\n",
    "    u2 = LinearUtilityFunction(\n",
    "        [0, 1], reserved_value=0.0, outcome_space=m.outcome_space\n",
    "    )\n",
    "    m.add(NashNegotiator(ufun=u1, name=\"a1\"))\n",
    "    m.add(NashNegotiator(ufun=u2, name=\"a2\"))\n",
    "    result = m.run()\n",
    "    u1values[i], u2values[i] = u1(result.agreement), u2(result.agreement)\n",
    "    a1values[i], a2values[i] = result.agreement\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(slopes, u1values, label=\"First negotiator\")\n",
    "plt.plot(slopes, u2values, label=\"Second negotiator\")\n",
    "plt.plot(slopes, u1values + u2values, label=\"Welfare\")\n",
    "plt.ylabel(\"Utility received\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Slope value for first negotiator\")\n",
    "plt.show()\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(slopes, a1values, label=\"First negotiator\")\n",
    "plt.plot(slopes, a2values, label=\"Second negotiator\")\n",
    "plt.ylabel(\"Agreement Reached\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Slope value for first negotiator\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fc0996",
   "metadata": {
    "papermill": {
     "duration": 0.034424,
     "end_time": "2022-02-19T02:34:25.049007",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.014583",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "Notice that in this case, the both negotiators always get their maximum possible utility which leads to a linear increas in \n",
    "welfare with slope. There is a small exception though at slope zero.  Try running the last simulation\n",
    "several times. Does the peculiar result at slope zero persist? Does it lead to the same welfare every time? \n",
    "Can you explain it?\n",
    "\n",
    "### Rubinstein Bargaining Protocol (Stateful Mechanism)\n",
    "\n",
    "Rubinstein provided one of the earliest and most widely cited results for multi-round bilateral negotiation.\n",
    "\n",
    "In this protocol, two negotiators are again trying to find an agreement each maximizing its own utility. This is a full\n",
    "information game in which the utility function of both agents is common knowledge. Moreover, there is some discount \n",
    "mechanism that reduces the utility of any potential agreement over time which is different for the two negotiators but \n",
    "is also known. In this tutorial we will focus on the case with exponential discounting (with with $t$  representing \n",
    "round number and $\\delta_i$ is the discount factor for negotiator $i$). Throughout this section we will use superscripts to indicate round number. Moreover, we will focus on the case where the\n",
    "initial utility of an agreement is the value assigned to the agent in that agreement:\n",
    "\n",
    "$$\\tilde u_i^t(\\omega) = \\delta_i^t \\omega_i$$\n",
    "\n",
    "We can start by designing the `MechanismState` class corresponding to this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e8ef35ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:25.112991Z",
     "iopub.status.busy": "2022-02-19T02:34:25.112113Z",
     "iopub.status.idle": "2022-02-19T02:34:25.114236Z",
     "shell.execute_reply": "2022-02-19T02:34:25.114674Z"
    },
    "papermill": {
     "duration": 0.0356,
     "end_time": "2022-02-19T02:34:25.114854",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.079254",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@define\n",
    "class RubinsteinMechanismState(MechanismState):\n",
    "    discounts = field(default=list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66abf69a",
   "metadata": {
    "papermill": {
     "duration": 0.040651,
     "end_time": "2022-02-19T02:34:25.195262",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.154611",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "We can then define the mechanism class itself:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "725c538a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:25.292047Z",
     "iopub.status.busy": "2022-02-19T02:34:25.290894Z",
     "iopub.status.idle": "2022-02-19T02:34:25.293264Z",
     "shell.execute_reply": "2022-02-19T02:34:25.293729Z"
    },
    "papermill": {
     "duration": 0.056993,
     "end_time": "2022-02-19T02:34:25.293924",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.236931",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class RubinsteinMechanism(Mechanism):\n",
    "    \"\"\"Simplified Rubinstein's Mechanism with Exponential discounting\"\"\"\n",
    "\n",
    "    def __init__(self, extended=False, **kwargs):\n",
    "        kwargs.update(\n",
    "            dict(\n",
    "                issues=[\n",
    "                    make_issue(values=(0.0, 1.0), name=\"first\"),\n",
    "                    make_issue(values=(0.0, 1.0), name=\"second\"),\n",
    "                ],\n",
    "                max_n_agents=2,\n",
    "                dynamic_entry=False,\n",
    "                state_factory=RubinsteinMechanismState,\n",
    "            )\n",
    "        )\n",
    "        super().__init__(**kwargs)\n",
    "        self.add_requirements(dict(propose=True, set_index=True))\n",
    "        self.state.discounts = []\n",
    "        self.proposals = []\n",
    "        self.extended = extended\n",
    "\n",
    "    def add(\n",
    "        self,\n",
    "        negotiator: \"Negotiator\",\n",
    "        *,\n",
    "        discount: float = 0.95,\n",
    "        **kwargs,\n",
    "    ) -> Optional[bool]:\n",
    "        weights = [1, 0] if len(self.negotiators) == 0 else [0, 1]\n",
    "        ufun = ExpDiscountedUFun(\n",
    "            LinearUtilityFunction(weights, outcome_space=self.outcome_space),\n",
    "            outcome_space=self.outcome_space,\n",
    "            discount=discount,\n",
    "        )\n",
    "        added = super().add(negotiator, ufun=ufun, role=None, **kwargs)\n",
    "        if added:\n",
    "            self.state.discounts.append(discount)\n",
    "\n",
    "    def round(self) -> MechanismRoundResult:\n",
    "        \"\"\"One round of the mechanism\"\"\"\n",
    "        if self.current_step == 0:\n",
    "            if len(self.negotiators) != 2:\n",
    "                return MechanismRoundResult(\n",
    "                    error=True,\n",
    "                    error_details=f\"Got {len(self.negotiators)} negotiators!!\",\n",
    "                    broken=True,\n",
    "                )\n",
    "            for i, n in enumerate(self.negotiators):\n",
    "                n.set_index(i)\n",
    "        outcomes = list(n.propose(self.state) for n in self.negotiators)\n",
    "        self.proposals.append(outcomes)\n",
    "        if any(o is None for o in outcomes):\n",
    "            return MechanismRoundResult(broken=True)\n",
    "        if sum(outcomes[0]) <= 1 + 1e-3:\n",
    "            if self.extended:\n",
    "                if (\n",
    "                    outcomes[0][0] <= outcomes[1][0] + 1e-5\n",
    "                    and outcomes[1][1] <= outcomes[0][1] + 1e-5\n",
    "                ):\n",
    "                    return MechanismRoundResult(\n",
    "                        agreement=(\n",
    "                            min(outcomes[0][0], outcomes[1][0]),\n",
    "                            min(outcomes[0][1], outcomes[1][1]),\n",
    "                        )\n",
    "                    )\n",
    "            elif max(abs(outcomes[0][i] - outcomes[1][i]) for i in range(2)) < 1e-3:\n",
    "                return MechanismRoundResult(\n",
    "                    agreement=tuple(\n",
    "                        0.5 * (outcomes[0][i] + outcomes[1][i]) for i in range(2)\n",
    "                    )\n",
    "                )\n",
    "\n",
    "        return MechanismRoundResult()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1941edf5",
   "metadata": {
    "papermill": {
     "duration": 0.052017,
     "end_time": "2022-02-19T02:34:25.398407",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.346390",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "The mechanism is very similar to the Nash Bargaining Game with few modifications:\n",
    "\n",
    "1. The constructor passes the `RubnisteinMechanismState` as the `state_factory`. We also create explicit issues for the \n",
    "   negotiation. The number of steps is not limited to $1$. Note that we define two requirements for any negotiator that \n",
    "   wants to engage in this protocol.\n",
    "1. The `add()` method now creates the utility function for the negotiator following the rules of the game. Each\n",
    "   negotiator receives an exponentially discounted utility function of the portion it receives from the pie.\n",
    "1. We override `extra_state` to provide the `discounts` values to the state factory.   \n",
    "1. `propose()` in the negotiators is expected to receive a state of type `RubinsteinMechanismState` .\n",
    "1. Each round all negotiators propose outcomes and the negotiation terminates with success only if both proposals are\n",
    "   feasible (sum to no more than 1.0) and equal (approximately)\n",
    "\n",
    "We can now develop the base negotiator type for this mechanism:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "780fc829",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:25.496414Z",
     "iopub.status.busy": "2022-02-19T02:34:25.495517Z",
     "iopub.status.idle": "2022-02-19T02:34:25.497764Z",
     "shell.execute_reply": "2022-02-19T02:34:25.498385Z"
    },
    "papermill": {
     "duration": 0.04658,
     "end_time": "2022-02-19T02:34:25.498690",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.452110",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class RubinsteinNegotiator(Negotiator):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.add_capabilities(dict(propose=True, set_index=True))\n",
    "        self.my_index = -1\n",
    "\n",
    "    def set_index(self, indx: i) -> None:\n",
    "        self.my_index = indx\n",
    "\n",
    "    @abstractmethod\n",
    "    def propose(self, state: RubinsteinMechanismState) -> Outcome:\n",
    "        \"\"\"Proposes an outcome which is a tuple of two numbers between zero and one\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3227bb74",
   "metadata": {
    "papermill": {
     "duration": 0.032523,
     "end_time": "2022-02-19T02:34:25.563621",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.531098",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "The base negotiator here implements `set_index` so that specific negotiators need not bother about it. It defines a single abstract method to be overriden by any compatible negotiator.\n",
    "\n",
    "We will first define a utility function to plot what happens in a negotiation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6f4a6298",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:25.657292Z",
     "iopub.status.busy": "2022-02-19T02:34:25.656373Z",
     "iopub.status.idle": "2022-02-19T02:34:25.658987Z",
     "shell.execute_reply": "2022-02-19T02:34:25.659422Z"
    },
    "papermill": {
     "duration": 0.065905,
     "end_time": "2022-02-19T02:34:25.659608",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.593703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_a_run(mechanism: RubinsteinMechanism) -> None:\n",
    "    result = mechanism.state\n",
    "    x = np.linspace(0.0, 1.0, 101, endpoint=True)\n",
    "    first = np.array([_[0] for _ in mechanism.proposals])\n",
    "    second = np.array([_[1] for _ in mechanism.proposals])\n",
    "    plt.plot(x, 1 - x, color=\"gray\", label=\"Pareto-front\")\n",
    "    plt.xlabel(\"Agent 1's utility\")\n",
    "    plt.ylabel(\"Agent 2's utility\")\n",
    "    plt.scatter(\n",
    "        first[:, 0], first[:, 1], marker=\"x\", color=\"green\", label=\"Proposals from 1\"\n",
    "    )\n",
    "    plt.scatter(\n",
    "        second[:, 0], second[:, 1], marker=\"+\", color=\"blue\", label=\"Proposals from 2\"\n",
    "    )\n",
    "    if result.agreement is not None:\n",
    "        plt.scatter(\n",
    "            [result.agreement[0]],\n",
    "            [result.agreement[1]],\n",
    "            marker=\"o\",\n",
    "            color=\"red\",\n",
    "            label=\"Agreement\",\n",
    "        )\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c883cb0",
   "metadata": {
    "papermill": {
     "duration": 0.033617,
     "end_time": "2022-02-19T02:34:25.727603",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.693986",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's implement a random negotiator that ends the negotiation if it finds that it is impossible to get a positive \n",
    "utility anymore (due to discounting) and otherwise returns a random apportionment of the pie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b7e5d16f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:25.795759Z",
     "iopub.status.busy": "2022-02-19T02:34:25.794853Z",
     "iopub.status.idle": "2022-02-19T02:34:25.797051Z",
     "shell.execute_reply": "2022-02-19T02:34:25.797499Z"
    },
    "papermill": {
     "duration": 0.039252,
     "end_time": "2022-02-19T02:34:25.797679",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.758427",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class RandomRubinsteinNegotiator(RubinsteinNegotiator):\n",
    "    def propose(self, state: RubinsteinMechanismState) -> Outcome:\n",
    "        if self.ufun((1.0, 1.0)) < 0.0:\n",
    "            return None\n",
    "        r = random()\n",
    "        return r, 1 - r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113e5235",
   "metadata": {
    "papermill": {
     "duration": 0.030565,
     "end_time": "2022-02-19T02:34:25.857759",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.827194",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "Now we can run negotiations using our new protocol and negotiator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7ed1acc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:25.970899Z",
     "iopub.status.busy": "2022-02-19T02:34:25.941773Z",
     "iopub.status.idle": "2022-02-19T02:34:26.169410Z",
     "shell.execute_reply": "2022-02-19T02:34:26.169893Z"
    },
    "papermill": {
     "duration": 0.283359,
     "end_time": "2022-02-19T02:34:26.170085",
     "exception": false,
     "start_time": "2022-02-19T02:34:25.886726",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agreed to: (0.6640412499599437, 0.33595875004005626) after 148 steps\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mechanism = RubinsteinMechanism(extended=False)\n",
    "mechanism.add(RandomRubinsteinNegotiator(), discount=0.75)\n",
    "mechanism.add(RandomRubinsteinNegotiator(), discount=0.75)\n",
    "print(f\"Agreed to: {mechanism.run().agreement} after {mechanism.current_step} steps\")\n",
    "plot_a_run(mechanism)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541ba029",
   "metadata": {
    "papermill": {
     "duration": 0.033818,
     "end_time": "2022-02-19T02:34:26.236715",
     "exception": false,
     "start_time": "2022-02-19T02:34:26.202897",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "Rubinstein showed in 1982 that there is a single perfect game equilibrium  of single round that takes the form:\n",
    "\n",
    "$$\\left(\\frac{1-\\delta_2}{1-\\delta_1\\delta_2}, \\frac{\\delta_2\\left(1-\\delta_1\\right)}{1-\\delta_1\\delta_2}\\right)$$\n",
    "\n",
    "We can implement the optimal negotiator for this mechanism as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f15d7fbb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:26.310608Z",
     "iopub.status.busy": "2022-02-19T02:34:26.309264Z",
     "iopub.status.idle": "2022-02-19T02:34:26.313856Z",
     "shell.execute_reply": "2022-02-19T02:34:26.314970Z"
    },
    "papermill": {
     "duration": 0.046592,
     "end_time": "2022-02-19T02:34:26.315262",
     "exception": false,
     "start_time": "2022-02-19T02:34:26.268670",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agreed to: (0.5128205128205131, 0.4871794871794869) in 1 steps\n"
     ]
    }
   ],
   "source": [
    "class OptimalRubinsteinNegotiator(RubinsteinNegotiator):\n",
    "    def propose(self, state: RubinsteinMechanismState) -> Outcome:\n",
    "        first = (1 - state.discounts[1]) / (1 - state.discounts[1] * state.discounts[0])\n",
    "        return first, 1 - first\n",
    "\n",
    "\n",
    "mechanism = RubinsteinMechanism()\n",
    "mechanism.add(OptimalRubinsteinNegotiator())\n",
    "mechanism.add(OptimalRubinsteinNegotiator())\n",
    "print(f\"Agreed to: {mechanism.run().agreement} in {mechanism.current_step} steps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853b9311",
   "metadata": {
    "papermill": {
     "duration": 0.053221,
     "end_time": "2022-02-19T02:34:26.431717",
     "exception": false,
     "start_time": "2022-02-19T02:34:26.378496",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "We can see that even though both negotiators had the same time-pressure ($\\delta_0 = \\delta_1$)  and have the same\n",
    "utility function, the negotiator that started, gets a higher utility at the equilibrium. \n",
    "\n",
    "\n",
    "Let's try to make an agent that does not use the information about the other agent's "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5ed069c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:26.533072Z",
     "iopub.status.busy": "2022-02-19T02:34:26.523265Z",
     "iopub.status.idle": "2022-02-19T02:34:26.752522Z",
     "shell.execute_reply": "2022-02-19T02:34:26.751477Z"
    },
    "papermill": {
     "duration": 0.279378,
     "end_time": "2022-02-19T02:34:26.752765",
     "exception": false,
     "start_time": "2022-02-19T02:34:26.473387",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agreed to: (0.49504950495049505, 0.49504950495049505) in 51 steps\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class AspirationRubinsteinNegotiator(RubinsteinNegotiator):\n",
    "    def __init__(self, *args, aspiration_type=\"linear\", max_aspiration=1.0, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self._asp = PolyAspiration(max_aspiration, aspiration_type)\n",
    "\n",
    "    def propose(self, state: RubinsteinMechanismState) -> Outcome:\n",
    "        if self.ufun((1.0, 1.0)) < 0.0:\n",
    "            return None\n",
    "        r = self._asp.utility_at(state.relative_time)\n",
    "        return (r, 1.0 - r) if self.my_index == 0 else (1.0 - r, r)\n",
    "\n",
    "\n",
    "mechanism = RubinsteinMechanism(n_steps=100, extended=True)\n",
    "mechanism.add(AspirationRubinsteinNegotiator())\n",
    "mechanism.add(AspirationRubinsteinNegotiator())\n",
    "result = mechanism.run()\n",
    "print(f\"Agreed to: {result.agreement} in {mechanism.current_step} steps\")\n",
    "plot_a_run(mechanism)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c7d1cf",
   "metadata": {
    "papermill": {
     "duration": 0.032913,
     "end_time": "2022-02-19T02:34:26.817713",
     "exception": false,
     "start_time": "2022-02-19T02:34:26.784800",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "Now we can see when the first negotiator is a `conceder` :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a23a70d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T02:34:26.896621Z",
     "iopub.status.busy": "2022-02-19T02:34:26.895650Z",
     "iopub.status.idle": "2022-02-19T02:34:27.119630Z",
     "shell.execute_reply": "2022-02-19T02:34:27.120104Z"
    },
    "papermill": {
     "duration": 0.271146,
     "end_time": "2022-02-19T02:34:27.120295",
     "exception": false,
     "start_time": "2022-02-19T02:34:26.849149",
     "status": "completed"
    },
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agreed to: (0.27438013387778515, 0.7227722772277227) in 28 steps\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mechanism = RubinsteinMechanism(n_steps=100, extended=True)\n",
    "mechanism.add(AspirationRubinsteinNegotiator(aspiration_type=\"conceder\"))\n",
    "mechanism.add(AspirationRubinsteinNegotiator())\n",
    "print(f\"Agreed to: {mechanism.run().agreement} in {mechanism.current_step} steps\")\n",
    "plot_a_run(mechanism)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6264688",
   "metadata": {
    "papermill": {
     "duration": 0.037126,
     "end_time": "2022-02-19T02:34:27.194126",
     "exception": false,
     "start_time": "2022-02-19T02:34:27.157000",
     "status": "completed"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "source": [
    "As expected, the agreement shifted toward the second agent.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63e65725",
   "metadata": {
    "papermill": {
     "duration": 0.035265,
     "end_time": "2022-02-19T02:34:27.266682",
     "exception": false,
     "start_time": "2022-02-19T02:34:27.231417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 9.043357,
   "end_time": "2022-02-19T02:34:27.723886",
   "environment_variables": {},
   "exception": null,
   "input_path": "/Users/yasser/code/projects/negmas/notebooks/tutorials/03.develop_new_mechanism.ipynb",
   "output_path": "/Users/yasser/code/projects/negmas/notebooks/tutorials/03.develop_new_mechanism.ipynb",
   "parameters": {},
   "start_time": "2022-02-19T02:34:18.680529",
   "version": "2.3.4"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}